Although a number of studies have been conducted on extreme precipitation trends in different parts of the world including Iran, a great number of such studies have reported only the total amount of daily precipitation greater than a certain percentage (e.g., 95%) of the long term data (R95p), ignoring other useful indices. To address this research gap, we used other modified indices, namely R95_tot (fractional contribution of very wet days to annual total amounts), R95_tt (fractional contribution of very wet days to the total annual obtained from fitted gamma probability distribution), and RS95 (same as R95_tt except that it uses Weibull distribution and very wet days defined by 95 percentage of an individual year) by which the spatial and temporal changes of very wet days across Iran was assessed, 1985–2013. In addition, to evaluate the effect of the selected distribution on the results, a new index‐(RS95_gm)—was introduced and reported. This index is similar to RS95, except that it uses gamma distribution instead of Weibull. According to trend analysis of R95p, R95_tot, and R95_tt, reduced frequency of extreme precipitation events was detected in some northwest, west and northeast parts of Iran. On the contrary, RS95 (RS95_gm) results showed a higher frequency of extreme events across Iran. It was also demonstrated that while R95p, R95_tot, and R95_tt were unequivocally affected by changes in the mean wet‐day/ annual total precipitation, RS95 (RS95_gm) was more influenced by changes in the distributional shape, showing more stable trends. Although RS95 and RS95_gm were highly correlated with only 19% difference on average, their trend analysis results were not completely consistent (70% agreement). Thus, it may be concluded that any changes in statistical distribution in the calculation of the RS95 would have a considerable effect on whether the obtained trend is significant or not.

Time of concentration (TC) is a parameter in runoff estimation, used to study and design different types of projects in watersheds. Any error in TC calculation leads to an inaccurate estimation of the design flow, which can lead to over-sizing or under-sizing of designed facilities that can have great economic and environmental consequences. Therefore, choosing the correct method to estimate TC is of great importance. Due to the diversity of estimation methods in the literature, the obtained TC values are different. This study aims to present a new method to calculate TC, based on its main concept, i.e., the time required for a water parcel to reach its outlet from the farthest hydrological point of a watershed. A two-dimensional hydraulic simulation was used to model the water parcel travel. A watershed was selected as a case study, and its time of concentration was determined by salt solution tracing. The field measurement results were used for calibration of the numerical simulation model. Meanwhile, 31 empirical relations in the literature were reviewed to determine the most accurate ones. Estimated TC values were compared with the measured ones, and the relative error percentage was used to evaluate the accuracy of the result. In the empirical TC methods, the maximum error was above 300%, and the minimum error was 6.7% for the field studied area. The relative errors of hydraulic simulation outputs were between 3 and 27%. The results showed that only three empirical methods, namely Simas and Hawkins, SCS_lag, and Yen and Chow, had the least errors respectively equal to 6.7%, 8.660%, and 13.5%, which can be recommended for the studied area and those with similar hydrological characteristics. On the other hand, hydraulic simulation is also introduced as an efficient method to determine TC which can be used in any desired watershed.

There have been some correlations in the literature to predict the gas and liquid flow rate through wellhead chokes under subcritical flow conditions. The majority of these empirical correlations have been developed based on limited production data sets that were collected from a small number of fields. Therefore, these correlations are valid within the parameter variation ranges of those fields. If such correlations are used elsewhere for the prediction of the subcritical choke flow performance of the other fields, significant errors will occur. Additionally, there are only a few empirical correlations for sub-critical choke flow performance in high rate gas condensate wells. These led the authors to develop a new empirical correlation based on a wider production data set from different gas condensate fields in the world; 234 production data points were collected from a large number of production wells in twenty different gas condensate fields with diverse reservoir conditions and different production histories. A non-linear regression analysis method was applied to their production. The new correlation was validated with a new set of data points from some other production wells to confirm the accuracy of the established correlation. The results show that the new correlation had minimal errors and predicted the gas flow rate more accurately than the other three existing models over a wider range of parameter variation ranges.

Eggplant is an important product in greenhouse cultivation in the world. However, much of its production is done on the farm. Few studies have been conducted on modelling of its growth and yield. Considering the simplicity of the logistic models, only the temperature and dry matter data during the growing season are needed to calibrate them. Hence, we evaluated the performance of the logistic model in growth and yield prediction of eggplant in greenhouse and farm conditions. Eggplants were planted in 2012 and 2013 under different treatments of irrigation and salinity with a complete randomized block statistical design. Irrigation frequency treatments consisted of: daily (I1), weekly, (I2) and every 2 weeks of irrigation (I3). Each pot was irrigated to field capacity level. Four levels of salinity treatments are as follows: electrical conductivities (EC) of 0.8 (J1); 2.5 (J2); 5.0 (J3) and 7.0 (J4) dS∙m-1. The amount of plant dry matter was measured during the growing season (DM) and the amount of product (Y) at the end of the growing season. The logistic model was calibrated with the first-year data and validated with the second-year data. Logistic equation coefficients and harvest index were estimated as a function of the depth and electrical conductivity of irrigation water. The results showed that the accuracy of the logistic model for estimating DM during the growing season was good and predicted the product at the end of the growing season with acceptable accuracy. Also, the model's agreement with the measured DM and Y was good.