نشریه دانش آب و خاک
سال سی و چهارم شماره 1 (بهار 1403)

Due to population growth and Iran's location in arid and semi-arid regions of the world, the need for water and food has increased and as a result, the pressure on water and soil resources will be more than before. On the other hand, the risk of drying up Lake Urmia, which causes environmental problems in the region, requires macro-water planning for the region and the use of optimal cultivation pattern to deal with water scarcity. Therefore, optimal use of water preserves water resources and increases the quality of products. Today more than ever, increasing the production of strategic crops such as wheat requires the proper use of water resources. The main source of food for the Iranian people is wheat and related products, and any action that increases the yield of wheat due to limited soil resources, especially water resources, is important and necessary at the same time. In recent years, significant advances have been made in modeling product growth and development using mechanical models. Plant growth models are increasingly used in the analysis of agricultural systems and simulate the plant's response to growth factors using mathematical equations. The AquaCrop model is one of the dynamic and user-friendly models developed by the FAO. The AquaCrop model receives information about farm, plant, soil, irrigation and climate, and ultimately predicts important parameters such as crop. Wheat yield simulation allows efficient management and better planning under various environmental inputs such as soil and water. To achieve higher accuracy and less model error, field parameters must be properly calibrated by the model to achieve proper performance. Also, calibration of the model, if not done correctly, causes a high error prediction by the model, which leads to incorrect management, water loss, plant drought and other cases. Therefore, using a model that has accurate and close prediction to the AquaCrop model and requires fewer input parameters is essential, which saves time, reduces costs and eliminates calibration errors. However, this model requires relatively large input parameters and is a time-consuming model in the presence of multiple scenarios.3

In recent years, smart models have been able to show high accuracy and become reliable models. Therefore, in the present study, to solve this problem and develop a model with less input data, using the ANN, SVR and SVR-FFA intelligent models and creating 440 scenarios in 2 farms, the performance of the AquaCrop model was compared.99WestW2 farm is located in Miandoab city and has a yield of 6.588 (ton ha-1) and WestW10 farm is located in Mahabad city and has a yield of 5.05 (ton ha-1).

The results of the model are performed using 5 evaluation criteria of Correlation coefficient, Root mean square error, Nash-Sutcliffe coefficient, Wilmot’s index of agreement and, Mean absolute percentage error. The results of this study showed that for both 99WestW2 and WestW10 farms, the SVR-FFA3 model could have the lowest error rate, so that for the yield index, the RMSE value for the mentioned farms was 0.033 and 0.069 (ton ha-1), respectively. The use of three models SVR, SVR-FFA, and ANN and their comparison with the AquaCrop model to predict wheat yield has been done for the first time in this study. The SVR model was able to show the highest accuracy after the SVR-FFA model. For 99WestW2 farm, it can reduce the error rate to 0.043 (ton ha-1) and for WestW10 farm to 0.077 (ton ha-1) and show good performance. The ANN model, after the SVR model, was able to show acceptable accuracy. The ANN model for 99WestW2 farm was able to reduce the error rate to 0.123 (ton ha-1) and for WestW10 farm to 0.094 (ton ha-1). Finally, the ANN model had a relatively higher error than the SVR-FFA and SVR models, respectively, and showed a relatively lower performance than the two models.

Finally, the intelligent SVR-FFA, SVR and ANN models, despite having the least number of inputs, were able to predict yield values in the shortest time and with the highest accuracy. However, the results showed that the lower the model inputs, the weaker the model prediction. For further studies, it is suggested that the ANN model be combined using the firefly algorithm (MLP-FFA) to increase the accuracy of the ANN model and make more accurate predictions of wheat yield.

One of the solutions to sustainable agriculture is the use of mushroom substrate compost (MSC). Mushroom substrate compost is a mixture of stable organic matter and composed of various components such as wheat straw, poultry manure and gypsum. It is rich in nutrients, such as nitrogen, ammonium nitrate, superphosphate, potassium salts and etc. MSC could be a good alternative for other fertilizers and amendments due to less cost and improving soil properties. However, its simultaneous effect in three levels of 0, 3 and 6 % on the properties of soils with different textures has not been before reported. Therefore, objective of this study was to evaluate the short-term effect of the MSC on some physical and chemical properties of soil with different textures.

In this study, three soil samples with different textures, sandy loam, loam and clay, were taken from a bayer farm of the Agricultural Research Center, Abbas Abad Farm of the Bu-Ali Sina University and a bayer farm in the Kerk Suffla Village of Nahavand County, respectively. Sampling was done with a knowledge of soil properties, from soil surface layer (0-20 cm depth). The soil samples were transferred to the Soil Physics Laboratory of the Bu-Ali Sina University for testing and after being air dried, they were passed through a 4 mm sieve. The research was conducted as a factorial experiment in a completely randomized design with three replications. The factors were included soil texture at three levels (sandy loam, loam and clay), and MSC at three levels (0, 3 and 6% W/W). The MSC sample was obtained from Alvand Mountain Company in Hamadan Industrial City. The mushroom substrate consisted of wheat straw, lime and poultry manure, which were mixed at rates of 57, 36 and 7%, respectively. MSC levels were calculated based on treatments after determining the desired soil content for each container. The treated soils were transferred into plastic containers with dimensions of 21 × 13.5 ×12.5 cm based on the field bulk density of each soil. A total of 27 plastic containers containing treatments were prepared. Soils treated in plastic containers were saturated with tap water and then dried for a 120-day incubation period.

The results showed that the order of the soil total porosity was loam > clay > sandy loam, with significant difference between them. The application of mushroom substrate compost significantly increased the porosity at 3 and 6% levels compared to the control. But there was no significant difference between total porosity at 3 and 6% levels. Also, the order of the bulk density in different textures was exactly opposite to that of total porosity. Application of 6% mushroom substrate compost significantly increased cation exchange capacity in sandy loam soil compared to that in the control and 3% level, but no significant difference was found between the cation exchange capacity of control and 3% level. The order of cation exchange capacity was clay > loam > sandy loam, with significant difference between them. Comparison of the electrical conductivity of the soils at different levels of MSC showed that at the zero level of the MSC, the order of electrical conductivity values was loamy > clay> sandy loam, with significant difference between them. The order of pH values was sandy loam > clay > loam at control. The order of pH values was sandy loam = clay > loam at the 3 and 6% levels of the MSC. The results showed that, soil cation exchange capacity, electrical conductivity and total porosity increased in the ranges from 11.8 to 36 cmolc kg-1, 0.16 to 0.69 dS m-1 and 0.52 to 0.56, respectively and bulk density and pH decreased in the ranges from 1.24 to 1.18 g cm-3 and 8.25 to 7.3, respectively, by using MSC. The greatest effect of treatments at the 6% level of MSC on the electrical conductivity, total porosity, and bulk density was obtained in the soil with loam texture, and on the pH and cation exchange capacity was obtained in soil with clay texture.

According to the results, mushroom substrate compost increased cation exchange capacity, electrical conductivity and total porosity and decreased pH and bulk density in all three soils by increasing MSC levels. Overall, the results showed that mushroom substrate compost improved soil physical and chemical properties, due to its stable organic matter and low bulk density. As this compost has a much lower price than other soil modifiers, therefore, mushroom substrate compost can be used in agricultural lands to maintain soil fertility, improve soil stability, and improve soil physical and chemical properties.

In the hydrologic cycle, evaporation is the primary step that causes water loss. Evaporation takes into account various parts of the water balance under completely different climates, and its correct prediction is very important for water resources management. The importance of evaporation and its impact on surface water balance is highlighted through its relation to climate change and global warming. The latest outputs of meteorological models suggest that global warming has caused an increase in evaporation from the land surface and surface water bodies, which is anticipated to have a serious impact over time on water resources management and the global population. In arid and semi-arid regions, accurate prediction of evaporation is very important for decision-makers due to water scarcity. Estimating daily evaporation with the highest accuracy and in the fastest possible time is essential to determine the water needs of different products, design irrigation programs, and manage water resources in different areas, especially when there is insufficient meteorological information. Evaporation has complex and non-linear behavior. Also, the evaporation parameter is not measured in some meteorological stations. Furthermore, meteorological stations are not correctly distributed in many developing countries including Iran. Since coastal areas have more evaporation than others, in many cases the amount of evaporation is higher than the global average. Despite the high importance of evaporation in coastal areas, very few studies have predicted this parameter in Iran. Moreover, accurate prediction of water loss in these areas leads to a better understanding of the hydrological cycle and is essential for optimal water management and agriculture. Thus, the purpose of this research is to predict daily evaporation values in four coastal stations of Abadan, Ramsar, Bandar Abbas, and Bandar Anzali.

The main meteorological parameters including average relative humidity, minimum relative humidity, maximum relative humidity average temperature, minimum temperature, maximum temperature, sunshine hours, and wind speed, under separate scenarios, as input for support of vector regression (SVR) and SVR with firefly algorithm (SVR-FFA) for estimating evaporation values were used on a daily scale. Statistical parameters in the time period of 1990-2021 were utilized as input to the mentioned models. In order to evaluate the performance of the implemented models, various statistical parameters were used, including correlation coefficient (R), root mean squared error (RMSE), Nash-Sutcliffe coefficient (NS), and Willmott's Index of Agreement (WI). To better estimate the daily evaporation values, eight different scenarios were used as the combinations of input parameters.

Based on the obtained results for all studied stations, the SVR-FFA-8 showed the least error with RMSE = 2.843 (mm day-1) for Abadan station, RMSE = 1.13 (mm day-1) for Ramsar station, RMSE = 1.985 (mm day-1) for Bandar Abbas station and RMSE = 1.225 (mm day-1) for Bandar Anzali station. For the indices of correlation coefficient, Nash-Sutcliffe coefficient, and Wilmott’s index of agreement, the SVR-FFA-8 model also indicated in the highest values between observed and predicted amounts. Also, the indices of correlation coefficient, Nash-Sutcliffe coefficient, and Wilmott’s index of agreement illustrated the highest accuracy in Abadan station for all combinations compared to other stations, which shows the high correlation of observed and predicted values in this station. After SVR-FFA-8, SVR-FFA-7 model in Abadan and Bandar Anzali stations and the SVR-FFA-6 in Ramsar and Bandar Abbas stations showed acceptable performance. Thus, the RMSE for Abadan and Bandar Anzali stations is 2.995 (mm day-1) and 1.272 (mm day-1), respectively, and for Ramsar and Bandar Abbas, 1.176 (mm day-1) and was obtained 1.993 (mm day-1). Comparing the results of SVR combinations also revealed that for Abadan, Ramsar, and Bandar Anzali stations, SVR-8 and for Bandar Abbas station, SVR-6 showed the highest accuracy among all SVR combinations in all four studied stations. Also, Ramsar station presented the lowest RMSE compared to other stations. After the SVR-8 model for Abadan, Ramsar, and Bandar Anzali stations, the SVR-7 and SVR-6 models for the Bandar Abbas station showed a weaker performance due to having less input parameters. The comparison between the input parameters also concluded that the sunny hours is the most important parameter in predicting the daily evaporation values in all four stations, thus increasing the accuracy of the models.

Climate change and its consequence impacts on the different phenomena of earth are serious mankind concerns during recent years. Climate change and global warming have very significant negative impacts on different resources including water and ice resources, forests, pastures, agricultural fields, industry and finally human life. Air temperature and precipitation variations are primary effects of climate change on the atmospheric elements. Hence, the assessment of the atmospheric element for an instance temperature has critical importance. Temperature rise caused by climate change have serious negative impacts on agricultural activities through increasing the evaporation and the possibility of droughts. Because climatological elements have nonlinear behavior and they are not function of a certain statistic distribution therefore a tendency for using non-parametric approaches especially Mann-Kendall is growing. The complicated nature of physical processes and lack of adequate knowledge in the climate models have caused creating statistical models and their development for defining these processes. The application of these models for reconstruction of past values and predicting of future values has been called time series. The aim of current research is analyzing the variation trend of mean maximum monthly temperature using Mann-Kendall test, mean maximum monthly temperature with time series method, determining proper pattern and prediction of temperature variations at the Northwest of Iran in the following years.

In this research the trend of mean maximum temperature variations in 12 selected stations in Northwest of Iran in a 24 years period was investigated. At first, the trend of variation data series for was tested using Mann-Kendall approach. Then, mean maximum monthly temperature was predicted using time series model. Minitab 17 software was applied in order time series model development and prediction purposes. Total number of data for each set was 285 where 80% of them were considered for calibration and 20% for model validation. The performance of models was investigated based on Model Efficiency Coefficient (CE) and Correlation Coefficient (R) indices. The CE varies between ∞- to 1 and the closer values to 1 indicates more accurate model performance. Finally, temperature predictions were done for following 8 years based on developed models.

The obtained results of application of Mann-Kendall test for determining mean maximum temperature trend in 12 studied stations in the Northwest of Iran clarified an increasing behavior for all stations. Increasing trends in Ahar and Sarab station were significant at the level of 95% and in the Tabriz, Marageh, Miyaneh, Ardabil, Khalkhal, Urmia, Khoy and Mahabad stations the significance level was 99%. Regarding to the basic assumptions in time series modeling, before starting model creating, the normal and static situation of data series was tested. The obtained results of these tests also showed a linear increasing trend in the investigated stations. Consequently, seasonal and non-seasonal differential process on initial series in the studied stations was conducted to model recognize through ACF and PACF differential series graphs. The temperature variations along different seasons of year in all stations proved more increasing for all stations in the winter in comparison with other seasons.
Considering 12th differential level due to seasonal characteristic of data, ACF and PACF graphs of differential series were plotted and a correlation was observed between data in first lag. To create series model, seasonal model of SARIMA(p,d,q)(P,D,Q)ω was applied. After calibration and validation of final models for studied stations, these models were applied to for predicting 8 following years (2018-2026) and were compared with basic period (1994-2017). According to the predictions, mean maximum temperature in all station shows an increasing in comparison to the basic period. The highest increasing amount is for Jolfa station with 4.39˚C and the lowest value was determined for Parsabad station with 0.69 ˚C. The variations of temperature was assessed in seasonal scale for 8 upcoming years. The comparisons of temperature variation for all stations in the different seasons showed increasing behaviors in all stations in winter in comparisons with other seasons.

Mean maximum temperature in 12 studied stations was modeled by time series. High values for R and CE in these stations proved high accuracy of this method for predicting of air temperature. After model development and selection of the most proper model for studied stations, the prediction of temperature was performed for 8 following years for each station. The temperature variations in this duration were investigated seasonally and the results showed that the maximum temperature increasing for all stations will occur in the winter. Temperature increasing in winter months may cause negative impacts like change in precipitation pattern from snow to rain, early melting of region snow reservoirs, incomplete vernalization of the seeds and early start of growing season with a risk of frost hazard for crops.

A combined assessment of drought risk and associated impacts on crop production based on a probabilistic approach seems appropriate to understand the multivariate nature of drought risk in agriculture. To overcome the problems caused by drought impact detection, several approaches have been developed in recent decades. Among the multivariate analysis approaches, copula functions are very popular. Copulas use univariate marginal distributions to form a joint distribution. The joint distribution can be described by the corresponding marginal distributions and copula functions that describe the dependency structure. In this research, using the statistical precipitation data in the Tabriz plain in eastern part of Lake Urmia basin, and yield of rainfed wheat in this area, a model based on copula functions was developed to determine the diffrent probabilities of yield risk and different drought conditions. Also, the application of copula functions related to rainfed wheat yield in this region was performed for the first time, and the presented method will be applicable to other areas and other crop cultivation.

In the case of meteorological drought, the basis for calculating the degree of drought is determined by comparing the amount of precipitation with the long-term average or its normal values. The SPI index is considered to be an appropriate and powerful index to use as a time scale droughts monitoring. Basically, SPI was created to detect the lack of precipitation on multiple time scales. Among the reasons that make the use of this index so popular, we can mention the standard nature of this index as it can be used in regional studies and establish a temporal relationship between drought events in different parts of the same area. The SPI index is a dimensionless index and its more negative values show the more severe the drought.
Analysis of variables individually is easy and can be analyzed by statistical distribution functions; but statistical analysis joint variables is very complicated and impossible in most cases. If the correlation criterion of these variables is known, their joint probability distribution can be obtained using copula functions. Using copula functions for modeling has a high degree of flexibility as it is possible to choose different marginal distributions to create a multivariate model. Copulas are functions that form a bivariate or multivariate distribution based on two or more univariate marginal functions. Several copula functions can be used to construct a two-dimensional joint distribution of hydrological and agricultural variables, among which Archimedean and elliptical copula families are the most commonly used. In the present study, six copula functions are used and the parameters of the paired functions are determined using the two-stage maximum likelihood method, which estimates the parameters of the marginal distribution and the copula function by forming two likelihood functions. In order to investigate joint probability of rainfed wheat yield and drought index, the time series of rainfed wheat yield in the Tabriz Plain region and SPI index during the last 30 years from 1990 to 2020 was applicated in this study.

In general, the improvement in agricultural methods, investments and technological advances during this period have led to a continuous increase in crop yield, however, a sharp drop in crop yield is clearly evident at times during the reporting period. In this research, to ensure that the observed trend does not affect the results, the Copula model was built using the detrended time series data by removing the trend in the values and the variance in the yield data. Based on the results obtained, crop production decreases dramatically during severe droughts, so such sensitivity to moisture deficits caused by low rainfall after several wet years can be attributed to farmers' expectations and management policies driven by high productivity during the previous ones wet years were determined.
To use series of standardized yield and growing season SPI in copula, the most appropriate distributions were selected and used, logistic and normal, respectively. Also, according to the calculated Kendall correlation (0.35), the best fit joint was Claytons function with AIC = -11.10, RMSE = 0.036 and used to construct the joint probability distribution of the standardized yield series of rainfed wheat and SPI of growth Period. Results showed that the cumulative probability of yield risk event in mild, moderate, severe, and very severe drought was 18.42, 7.82, 4.26, and 2.32 percent, respectively,and for an overall rainfed wheat yield risk and drought condition is about 33%. Meanwhile, the probability of rainfed wheat yield risk and non-drought events is only about 7% and joint probability of yield risk and SPI>1 is so close to zero.

In the current research, the probability of occurrence of the rainfed wheat yield risk and drought conditions was extracted by copula functions. Based on the results obtained, most changes in yield occur when the drought index is in the range greater than -1.5. In other words, in the conditions of severe and very severe drought, the yield of rainfed wheat does not show noticeable changes in probability. The opposite situation can also be observed for the situation of very wet years, that when there is high amounts of precipitation and the drought index show values higher than 1.5, the yield of the crop does not show much change. Thus, the SPI must be greater than one in order to achieve the desired yield and not be at risk of rainfed wheat yield, which in this study is considered to be a standardized yield values greater than zero. Because in this range of SPI, the joint probability of yield risk is estimated to be very close to zero. Therefore, this threshold can be introduced as the rainfed wheat yield safety threshold, but depending on the situation of the region and the drought index, the drought threshold (SPI<0) contains cumulative probability about 33% of the rainfed wheat yield risk.

Nitrogen (N) plays a major role in maize growth and yield. Therefore, adequate supply of N is required ‎for successful maize production. However, application of chemical nitrogen fertilizers is associated with ‎some problems such as groundwater ‎pollution, nitrogen enrichment of surface waters, and nitrate ‎accumulation in agricultural products. ‎Accordingly, nowadays a great attention has been paid to the ‎slow-release fertilizers. Nitrogen-enriched humic acids ‎‎(NHAs) are considered as promising slow-release ‎nitrogen fertilizers in agricultural systems. However, the ‎effects of these types of fertilizers on plant growth ‎and physiological characteristics have not been well ‎understood. For this purpose, the present study ‎investigates the effectiveness of NHAs on the ‎morphological and physiological characteristics of maize as ‎well as nitrogen loss through leaching. ‎

‎The Nitrogen-enriched humic acids (NHAs) were prepared through the simple process of nitration, and ‎from the reaction of nitric acid with humic acid (HA) ‎extracted from leonardite of Yazd Golsang Kavir ‎Company as an organic carbon source. Then, a ‎greenhouse experiment in a completely randomized ‎design (CRD) with three replications was conducted ‎to determine the effects of 16 treatments, including ‎control, urea (U1, U2 and U3), humic acid ‎‎(HA1, HA2 and HA3), nitrogen-enriched humic acid (NHA1, ‎NHA2 and NHA3), urea-humic acid (U1HA1, U2HA2 ‎and U3HA3), and urea-nitrogen-enriched humic acid ‎‎(U1NHA1, U2NHA2 and U3NHA3) on the morphological ‎and physiological characteristics of maize plant ‎‎(Single cross-704). The levels of treatments were ‎determined as the quarter (50 mg N kg-1), half (100 mg ‎N kg-1) and equal (200 mg N kg-1) to the maize ‎fertilizer requirement. In the combined treatments of urea ‎and HA or NHA, an equal fraction of the total ‎nitrogen was considered. After the end of the experiment, ‎using the standard methods, some ‎characteristics including root length, leaf area, plant height, root ‎volume, wet and dry weights of shoot ‎and root, leaf chlorophyll index, concentrations of phosphorus, ‎potassium, nitrogen and nitrate, and ‎nitrate reductase activity in both shoot and root were determined. ‎Moreover, during the experiment and ‎on given days, the maize pots were leached and the obtained ‎leachate was collected for the nitrate ‎measurement.

‎Findings

According to the results, the nitrogen content of the produced NHA (3.3%) was about two times‏ ‏higher ‎than the HA (1.6%). In addition, the NHA had higher‎‏ ‏carboxyl and phenolic hydroxyl content than the ‎HA. The FT-IR analysis showed the characteristic peaks of nitro (NO2) groups at wavenumbers of 1541 ‎and 1336 cm-1 in the spectrum of NHA. Germination test indicated that the NHA was not toxic to the maize ‎seeds. The results showed that the NHA treatments had a much better influence on the plant ‎morphological characteristics than ‎the ‏HA treatments. This observation may be due to the negative effects ‎of HA application at high dosages. In comparision, the UNHA treatments were only slightly more efficient ‎than the urea treatments. Combining‏ ‏NHA with urea diminishes the adverse impacts of separate ‎application of these two fertilizers. On average, leaf chlorophyll index and concentrations of total ‎nitrogen, nitrate and nitrate reductase enzyme in shoot part of plants in the NHA treatments were 11.5, ‎‎17.0, 35.2 and 29.4% higher than the HA tratments. The nitrate reductase concentration in the roots was ‎‎40.4% lower than the shoots. However, the UNHA and urea treatments showed almost similar efficiency ‎in improving physiological characteristics. The U3NHA3 or U3 treatments, i.e. the highest level of ‎nitrogen, showed the highest efficiency which means the high nitrogen requirements of maize in pot ‎experiments. Based on the results, the nitrogen supply to the maize plant increased the shoot ‎concentration of potassium higher than that of phosphorus. Although the U3 treatment indicated ‎the ‎highest nitrogen and nitrate concentrations in both root and shoot, the highest nitrate leaching was ‎also ‎observed for this treatment. However, by using the U3NHA3 treatment, the mean concentration of ‎nitrate ‎in the leachate decreased by about 48.7% as compared to the U3 treatment. ‎

‎Findings of this research revealed that the combined fertilizer of UNHA can be ‎a good alternative for ‎urea. It could not only supply nitrogen for plants, but could improve plant vegetative ‎growth, and in turn ‎considerably reduce nitrate leaching, which has highly beneficial effects on nitrogen use ‎efficiency as well ‎as environmental issues.‎

Evaporation is one of the most complex and important processes in studying hydrological and meteorological factors and plays a major role in determining the energy balance equations on the earth's surface. So, knowing the exact amount of evaporation volume is important for monitoring and correct management of water resources, irrigation planning, determining the irrigation needs, estimating evaporation from the reservoir of dams and modeling hydrological projects, especially in arid and semi-arid regions. On the other hand, modeling such a complex process in which many parameters interact with each other is so difficult that it is not possible to simplify this issue without multiple assumptions. Therefore, accurate estimation of evaporation has always been of great importance. Many experimental methods have been presented in estimating evaporation, but since these methods require a lot of input data or it is not possible to measure the variables in all areas, many of these methods have lost their effectiveness. Therefore, it is necessary to use methods which need fewer number of meteorological variables and estimate the evaporation with high accuracy. Therefore, the aim of the current research is to evaluate and present the most accurate model of evaporation estimation using three data-driven models in six synoptic stations in arid, semi-arid and humid climates of Iran, so that the proposed model, in addition to having sufficient accuracy, requires fewer input parameters to estimate evaporation even when there is no sufficient data.

In this regard, the ability of three machine-learning methods of gradient boosted tree (GBT), generalized linear model (GLM) and artificial neural network-multi layer perceptron (MLP) in estimating the amount of pan evaporation in dry (Yazd and Bafq stations), semi-arid (Birjand and Siah-Bisheh stations) and humid climates (Sari and Ferdous stations) were investigated. Daily parameters of some fundamental and effective meteorological variables on evaporation during the time period of 2001-2020 were collected. In order to investigate the possibility of using different combinations of meteorological parameters to estimate the evaporation as accurately as possible, six different combinations of meteorological parameters (average temperature, relative humidity, and wind speed and sunshine hours) were considered. Also, to evaluate the accuracy of the mentioned models, four assessment criteria were used including root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R) and scatter index (SI). Furthermore, diagrams of time series of the best models and the distribution diagram of observed and predicted pan evaporation by the models were presented and the most suitable combination of meteorological parameters that had suitable accuracy for estimating pan evaporation was suggested.

The results showed that in Birjand, Yazd, Ferdos, and Siah-Bisheh stations, MLP-VI with RMSE of 1.97, 1.95, 1.97, 2.91, respectively, performed more accurate than other studied models. Moreover, In Sari station MLP-IV and in Bafq station, MLP-V, with RMSE of 1.41 and 1.92, respectively, provided the most precise estimates of evaporation values. Finally, it can be comprehended that in all three studied stations, MLP provided the most accurate estimations of the amount of pan evaporation and it is suggested as a method with high degree of accuracy. Furthermore, GBT presented the weakest performance in comparison with other studied models. The mentioned trend about the high accuracy of the mentioned models for all studied stations can also be concluded from presented Figures. So, it can be inferred that the accurate models mentioned in each station had the least distribution around the bisector line and had the most accuracy and the least error. In other words, it is possible to estimate the evaporation values in all stations with the meteorological data of temperature, relative humidity, sunshine hours and wind speed with acceptable accuracy.

Evaporation is one of the main components of water balance in agriculture and is one of the effective and influential factors for suitable irrigation planning. Therefore, accurate estimation of this parameter has a significant role on reducing excessive water consumption. So, in this study, three data-driven models of MLP, GBT and GLM were implemented in six stations including Yazd, Birjand, Sari, Bafq, Siah-Bisheh and Ferdous. The obtained results indicated that the sixth scenario using all utilized meteorological parameters in Yazd, Birjand, Siah-Bisheh, and Ferdous stations, forth scenario in Sari and fifth scenario in Bafq station with the lowest error provided the most accurate estimates of the evaporation and may be recommended for proper estimation of pan evaporation values.

Estimation and prediction of scouring around the piers play a significant role to design these structures since with increasing dimensions of scour hole, stability of the pier is threatened; as a result, the structure may be destructed. In this study, scour hole in the vicinity of twin and three piers is estimated by using fuzzy c-means clustering of ANFIS (ANFIS-FCM) network technique. To do this, firstly, the parameters affecting scour hole around twin and three piers including Froude number (Fr), the ratio of the pier diameter to the flow depth (D/h), and the ratio of the distance between the piers to the flow depth (d/h) were detected. Subsequently, seven ANFIS-FCM models were defined by means of these dimensional input parameters. It should be stated that 70% of the experimental data were utilized to training the models and 30% of the rest were applied to testing. Next, the superior ANFIS-FCM model and the most important input parameter were introduced by implementing a sensitivity analysis. The premium model as a function of all input parameters simulated the scour values with a reasonable accuracy. For instance, the correlation coefficient (R), the scatter index (SI), and the Nash-Sutcliff efficiency coefficient (NSC) are respectively computed to be 0.988, 0.106, and 0.976. Furthermore, the Froude number was considered as the most important input parameter. Lastly, a computer code was introduced so as to simulate the scour hole around the twin and three piers.

The surface seal formation can have destructive agricultural, hydrological and environmental effects. Seal formation is a complex mechanism that controlled by a wide range of factors such as soil properties, rainfall characteristics, and flow conditions. The present research was conduct in order to identify an efficient index to determine the sensitivity of soil to the seal formation in the soil of Kowar plain region of Fars province.

In the present study, according to the area of the region, 160 sampling points selected, then according to the characteristics of the studied area and type of land use, to achieve the goals of the research, first, the land use, geology and topography maps of the area is prepared and by doing several times of field work in different seasons of the year, the topographies and shapes in the maps and pictures were adapted to the environmental conditions. To carry out this research, 80 composite soil samples (0-20 cm) were prepared from the Kovar plain of Fars province. Then the parameters of soil particle size distribution, mass moisture, organic matter, electrical conductivity and pH of saturated mud extract measured. Then the parameters of soil particles size distribution, volume wetness, organic matter, electrical conductivity and pH of saturated mud extract, measured. Also, mean weight diameter(MWD), geometric mean diameter(GMD), calcium carbonate equivalent, fractal dimensions, saturation percentage, sodium, calcium, magnesium, sodium absorption ratio (SAR), saturated hydraulic conductivity (Ks) and bulk density were determined. Assessing the sensitivity of the soil to the surface seal formation by comparing the regression equations of seven different indices, including the soil structural stability index (SSI), crusting index (CI), water aggregate stability (WAS), crusting susceptibility index (CSI), consistency index (C5 - C10), penetration resistance (PR) and relative sealing index (RSI) carried out in data preprocessing, Descriptive statistics of variables such as mean, maximum, minimum and variance, data distribution diagram and data distribution obtained using Minitab- 19 software. In addition, in case of remote data or mistakes in entering the data, appropriate measures taken and a table of descriptive information was prepared. The normality of the frequency distribution of these features evaluated using the significance test of skewness. To explain the ability of indicators and statistics in seal formation, First, linear correlation between variables determined by SPSS-22 software and by using Pearson's correlation coefficient (r), the correlation relationship between the indices and early characteristics of the soil was obtained and for data analysis, Pearson's correlation method and ridge multiple linear regression were used by stepwise backward method and using Statistica, SPSS-26 and Minitab-19 software.

In terms of the soil structural stability index, the studied area was in the danger range of aggregate destruction, which can considered relevant to the high percentage of silt in the region. The sodium surface absorption ratio of the majority of soil samples in the study area, had a non-sodium rating. 95% of the samples with agricultural land use in terms of organic matter were located in the weak to medium with structure and structure stability group. The majority of samples had more than 20% clay. Most of the measured variables had a low coefficient of variation and in terms of data distribution and dispersion, had a relatively favorable situation. Clay was the common variable in five regression equations of the indices, which refers to the importance and dual role of clay in the aggregates stability and reducing the sensitivity to compaction. The relatively low value of the standard deviation in the soil structural stability index indicated the greater accuracy of this model in estimating the coefficients. In the present study, the best-fitted regression model, to describe the sensitivity of the soil to the surface seal formation, compared to other models, belong to the soil structural stability and crusting indices.

Based on the results obtained, the soil aggregate stability index with the modified coefficient of explanation R2=0.92 had a high capability in predicting the sensitivity of the soil to the seal formation and is the most effective index in expressing the changes in soil aggregate stability and the surface sealing. The results of the data analysis expressing the extreme limitation class of the mean weight diameter was in the soil of the region.

One of the most critical environmental pollutants is oil contamination. This pollution affects biological characteristics as well as the physical and chemical properties of soil. Soil is a habitat for microbial communities whose abundance and diversity can be affected by petroleum hydrocarbons. Soil biological indicators including microbial respiration, are highly sensitive to environmental stresses and respond to them quickly. Microbial respiration is one of the most common biological indicators which is used to investigate the quality and health of the soil. Since petroleum hydrocarbons are toxic and persistent in soil, studying the pattern of changes in soil biological characteristics is important in effective soil management. The aim of this study was to investigate changes in the basal respiration (BR) and substarte induced respiration (SIR) of microbial communities in the presence of oil, and how petroleum hydrocarbons can disrupt microbial respiration. For this purpose, 120 samples of crude oil-contaminated soils were collected in the oil-rich area of Naft-Shahr (located in the west of Kermanshah province) which had natural and long-term oil pollution. After measuring the physicochemical properties of soil samples microbial respiration was measured by titration method.

In this research 120, oil-contaminated soil samples were used. According to the factors included in this experiment, a nested design was used to analyze the data. The test factors included locations (4 locations) and 3 different levels of oil pollution (L: low, M: moderate, and H: high). It should be mentioned that 10 replications were considered in three levels of oil pollution and a total of 120 soil samples were gathered in this study (4×3×10). The collected soils were analyzed for soil texture, pH, EC and organic carbon (OC), and carbonate calcium equivalent (CCE) using standard methods. The concentration of petroleum pollutants, were determined by the Soxhlet extractor. In order to investigate the abundance of culturable microbial population, bacterial counting was carried out in nutrient agar (NA) and carbon-free minimal medium (CFMM)+crude oil media. Basal and substrate-induced respiration were measured by the titration method. Backward regression coefficients were used in order to identify important independent variables affecting changes in BR and SIR. Finally, the results of measuring chemical, physical and biological parameters were analyzed using principal component analysis (PCA).

The experiments showed that the percentage of oil measured by the Soxhlet method for oil pollution levels (L, M, and H) were 4.03%, 9.95%, and 22.50%, respectively. The obtained results showed that basal and stimulated breathing increased with the increase in the intensity of pollution. Also, the microbial population showed a direct relationship with the increasing of the oil pollution. The highest measured BR and SIR respiration were obtained with values of 0.053 and 0.234 mgCO2/g.h, respectively, in heavily polluted soils .Multiple regression analysis of independent variables on BR and SIR showed that the most influential variable was oil percentage, which individualy explained 59% of BR variance and 72% of SIR variance. Principal components analysis (PCA) was also done and 73% of the density variance of the samples can be justified by the first two components (biochemical component and physical component).

In a summary, according to the microbial respiration results in oil-contaminated soil, the microbial population followed by microbial respiration increased with increasing oil pollutant concentration. It seems that long-term, aged and natural oil pollution has caused the selection of resistant microbial communities to the oil compounds, hence we can observe their positive response to the presence of oil compounds, and an increase in microbial respirations (BR and SIR).. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Soil plays an important role in the environment by controlling the fate and availability of organic compounds due to the ability to absorb these substances. The absorption and desorption of these substances in the soil is basically controlled by soil organic matter. Therefore, to clean up organic chemicals, it is important to study the absorption behavior of these substances by soil organic matter. Soil organic matter has a complex structure and composition that depends on the source of organic matter. Soil organic matter plays an important role in environmental processes, the transfer of natural and unnatural pollutants and their fate in the soil environment. Several studies have concluded that adsorbent properties can have a significant correlation with the adsorption of hydrophobic organic materials. For example, the adsorption of hydrophobic organic substances to soil organic substances is inversely proportional to the polarity of these substances. It has been reported that the absorption of chloroaliphatic chemicals decreases with the increase of oxygenated functional groups in the organic matter. The normal absorption coefficient of organic carbon (Koc) of naphthalene was inversely proportional to the polarity of organic matter ((O+N)/C) of different soils and sediments. Volatile organic compounds (VOCs) are organic chemical compounds that have a high vapor pressure under normal conditions, which causes these substances to quickly enter the atmosphere. Toluene is volatile organic compounds found in petroleum products such as diesel. These materials are well known for soil and surface water pollution. Toluene has been shown to cause various risks to human health such as carcinogenicity. Leakage of these materials during transportation and storage can pollute groundwater, land, soil, and atmosphere. The American Environmental Protection Agency has determined the maximum amount of toluene to be 1000 micrograms per liter. Volatile organic compounds are one of the main sources of polluting the environment, among which the compounds mentioned above are one of the most effective polluting substances due to their high consumption in industry and agriculture. Also, these materials are abundant in petroleum products such as gasoline and diesel. Humin has a high capacity to absorb petroleum hydrocarbons and can play the role of an absorbent for these substances in water and soil environments. These hydrocarbons are produced in large quantities in the industry. These substances pose harmful risks to the health of humans and living beings. The purpose of this research is to investigate the role of humin extracted from soil in absorbing toluene as a part of petroleum hydrocarbons. In order to better understand the movement process of these materials in the soil and to evaluate the effect of humin as a part of soil organic matter on volatile petroleum hydrocarbons, it is necessary to investigate the behavior and extent of absorption of these materials.

For extraction of humin, a soil with a high percentage of organic carbon was needed, and for this purpose, after examining the soils of northern Iran, the desired soil was sampled from the research forest of the University of Tehran with the financial classification of Sol and was air-dried for the next steps. After these steps, it was passed through a two mm sieve and stored in the laboratory. The chemical properties of soil and organic matter were measured, and the results are shown in Table 1. First, the soil sample was shaken for 48 hours with hydrochloric acid at a ratio of 1:10 on a reciprocating shaker and then centrifuged. Then the sample was shaken with distilled water on a shaker and centrifuged again. After completing the first step, the soil sample was mixed with a ratio of 1:10 with 0.1 normality NaOH for 24 hours shaken and then centrifuged, the supernatant solution was discarded and finally the above process until the supernatant solution of the sample turned pale yellow. This continued to be repeated 17 times. To investigate the interaction between toluene and humin, first, 0.1 g of humin was weighed and poured into a 120 ml vial. Then 30 ml of background solution was poured on the sample. Amounts of 0.2, 0.35, 0.5, 0.75, 1, 1.5 and 5 microliters were injected with a Hamilton syringe and closed with a rubber cap and sealed by an aluminum cover. In order to reach the equilibrium state, the samples were shaken at the equilibrium time obtained in the previous step on the shaking device with 150 revolutions and at a temperature of 25 ± 2 centigrade inside the incubator, and finally after reaching the equilibrium time, samples were taken by a gas syringe and the absorbed values was read.

Absorption of volatile hydrophobic substances is due to hydrophobic and van der Waals forces, and the reason for the initial increase is due to the empty surfaces of the absorbent, but later on, with the reduction of absorbent levels, the effect of the surface agent decreased and most of the hydrophobic forces were the agent of absorption. For this reason, the partition coefficient and absorption percentage decreased at once and then remained almost constant.

Fitting the absorption data with the surface absorption equations showed that the humin-toluene system had a good correlation with the Freundlich equation, and this correlation was well seen in the absorption of toluene. The absorption of toluene on humin was probably due to the roughness of the surfaces and little penetration in the structure of humin. In general, the absorption of hydrocarbon substances, especially volatile substances, on humic substances is a complex phenomenon. The absorption of toluene on humic material is only the absorption of a small part of hydrocarbon materials on a part of humic materials, and more research should be done on the absorption and desorption of these hydrocarbon materials on humic materials.

Soil particle size distribution (PSD) is one of the most important soil characteristics. Various fractal equations have been proposed for better description of this characteristic in recent decades. This study aimed to investigate the changes in fractal dimensions of PSD calculated with different equations in the Imamzadeh Ebrahim sub-watershed, Guilan province. To conduct this research, 93 soil samples were collected from different parts of the sub-watershed with different land use, soil erosion and vegetation covers. The PSD of the samples was measured. Three fractal models of Bird, Perrier-Bird, and Tyler-Wheatcraft were fitted to the data. The results showed that the Perrier-Bird and Bird models had less root mean square error (RMSE) than the Tyler-Wheatcraft model (RMSE was 8.3 for the Perrier-Bird and Bird models, while it was 29.3 for the Tyler-Wheatcraft model). The value of fractal dimension obtained from Bird model (2.73) was lower than the other two models (the fractal dimensions were 2.94 and 2.95 for Perrier-Bird and Tyler-Wheatcraft, respectively). The results of this study demonstrated that the fractal models showed different accuracy and precision under various lands. According to result, fractal dimension of all three models had a positive nonlinear relationship with clay while showing a negative linear relationship with sand. In general, it can be concluded that PSD and therefore the fractal dimension of PSD are function of soil type, vegetation cover and land use, and two-parameter models are more accurate to describe soil PSD due to their higher flexibility.

Oil contamination can be treated by physical, chemical and biological approaches. The first two methods have limitations such as high costs, inefficacy and altering natural ecosystem. Today, biological treatment is a more interesting process to remove petroleum contamination. Bioremediation is a technique in which biological systems such as microorganisms are applied to degrade or transform harmful chemicals. In recent years, employing hydrocarbon degrading bacteria to cleaning a petroleum contaminated soil has become a prevalent, efficient and cost effective technique that converts toxic wastes to non-toxic end products. Soil contamination with oil compounds such as heavy naphtha can threaten soil, environmental and human health. In bioremediation process, soil microorganisms use these hydrocarbons as a carbon source and, while making a microbial biomass, play a role in its decomposition and conversion to carbon dioxide. Furthermore, this type of contamination can affect soil microbial population and its enzyme activity. Soil microbiome, in turn, has effect on this contamination using relevant enzymes. Application and comparison of various bioremediation methods such as biostimulation, bioaugmentation and integrated treatment were the main aims of this study to bio-remove heavy naphtha from contaminated soil. Moreover, mmonitoring of soil enzyme activity changes (including dehydrogenase and lipase) in this condition under different bioremediation treatments was another goal of this research. For this purpose, in a heavy naphtha-contaminated sandy loam, a variety of bioremediation treatments, including biostimulation (including supply of nitrogen and phosphorus elements, addition of manure and Tween 80), bioaugmentation treatment (using a consortium of efficient bacteria) and integrated treatment (including all biostimulation and bioaugmentation treatments together) were tested.

In this study, a sandy loam soil was used. Heavy naphtha was applied at a rate of 7% V/W to soil samples and various bioremediation treatments were performed as mentioned above. This experiment was carried out in a pot scale (containing 3 kg soil) based on split plot factorial design (pollution factor, bioremediation factor and time) with 3 replications, at room temperature for 120 days. During the experiment, the pots were aerated once a week and the soil moisture content was adjusted to 70-80% (W/W) of the soil water holding capacity twice a week. For bioaugmentation of bacterial isolates, a bacterial suspension (108 cfu mL-1) having a consortium of Arthrobacter sp. COD2-3, Stenotrophomonas nitritireducens COD5-6، Stenotrophomonas asidamainiphila COD1-1 with a ratio of 5% V/W were used. In biostimulation treatment, cow manure with 5% W/W was used. In NP treatment, N and P elements from ammonium nitrate and potassium phosphate (K2HPO4), were used with a ratio of 20:5:1 (C: N: P), considering soil organic carbon. Tween 80 as surfactant in the relevant treatments, was used at a rate of 0.3% V/W. In the integrated treatment, all the mentioned treatments were used together. On days 0, 5, 10, 15, 30, 45, 60, 90 and 120, subsamples were taken from each pot to measure the activity of dehydrogenase and lipase enzymes.

The results showed that during the experiment, bioremediation treatments reduced heavy naphtha contamination and the highest value for removal rate of this compound was 81% in the integrated treatment. Contamination also affected the enzymatic activity of soil so that the activity of dehydrogenase and lipase in all bioremediation treatments showed a decreasing trend. Dehydrogenase enzyme activity in cow manure treatment decreased from 1.67 to 0.59 (μg TPF g-1 h-1) and activity of soil lipase enzyme decreased from 33.82 to 26.24 (mU g-1) in the integrated treatment during the experiment. Among the bioremediation treatments, cow manure and integrated treatment had a greater effect on the elimination of heavy naphtha contamination than other treatments (p <0.01). It seems that the use of the above treatments were able to remove more heavy naphtha by providing optimal nutritional, moisture and aeration conditions while intensifying the activity of soil microorganisms.

In this study, to reduce heavy naphtha contamination in the soil, bioremediation treatments including biostimulation, bioaugmentation and integrated approaches were used. According to the results of changes in dehydrogenase and lipase activity in naphtha-contaminated soil, each treatment was able to reduce contamination individually, while the integrated treatment was both biostimulatory and bioenhancing treatments. However, in the integrated treatment, the efficiency of bioremediation process was higher, due to the simultaneous use of biostimulation and bioaugmentation treatments. The use of integrated treatment in soils contaminated with petroleum compounds, including heavy naphtha, can help to biologically eliminate these contaminants.

Water quality assessment is one of the most important research and implementation issues in the world. Load continuity curve is a method that can achieve favorable results with less data in the field of determining the quality conditions of the river and its influencing factors. In this research, LDC curves were used to determine the pollutant sources affecting the Tajen River. First, with the help of probability distribution functions and stream discharge information (18 years), the best curves of flow continuity in two hydrometric stations including Rig Cheshme and Kordkhil were drawn, and then the curves of the maximum allowable load of nitrate pollutant for two agricultural uses and water ecosystem in the cultivation and non-cultivation seasons were created. Then the LDC curves were drawn for the 8-year period from 1390-91 to 1397-98.The results showed that in the area of Rig Cheshme station, most of the non-point sources affect the nitrate pollution of the river, however in Kordkhel station, due to the occasional increase in the amount of nitrate pollution for minimum discharges in the cultivation seasons, it was determined that non-point sources are the cause of this increase. The results of this study showed the favorable ability of load continuity curves in determining the origin of pollutant load. On the other hand, with the investigations done and according to the quality conditions of the river from Kordakhil station to the mouth of the Tajen river, it was found that the river is more polluted than the permissible limit near the mouth during the cultivation seasons. Therefore, the revision of the calculations of releasing the flow from the Shahid Rajaei dam, the optimal water consumption in the river, and the management of the application of nitrogen fertilizers in the lands of the region should be considered.Water quality assessment is one of the most important research and implementation issues in the world. Load continuity curve is a method that can achieve favorable results with less data in the field of determining the quality conditions of the river and its influencing factors. In this research, LDC curves were used to determine the pollutant sources affecting the Tajen River. First, with the help of probability distribution functions and stream discharge information (18 years), the best curves of flow continuity in two hydrometric stations including Rig Cheshme and Kordkhil were drawn, and then the curves of the maximum allowable load of nitrate pollutant for two agricultural uses and water ecosystem in the cultivation and non-cultivation seasons were created. Then the LDC curves were drawn for the 8-year period from 1390-91 to 1397-98.The results showed that in the area of Rig Cheshme station, most of the non-point sources affect the nitrate pollution of the river, however in Kordkhel station, due to the occasional increase in the amount of nitrate pollution for minimum discharges in the cultivation seasons, it was determined that non-point sources are the cause of this increase. The results of this study showed the favorable ability of load continuity curves in determining the origin of pollutant load. On the other hand, with the investigations done and according to the quality conditions of the river from Kordakhil station to the mouth of the Tajen river, it was found that the river is more polluted than the permissible limit near the mouth during the cultivation seasons. Therefore, the revision of the calculations of releasing the flow from the Shahid Rajaei dam, the optimal water consumption in the river, and the management of the application of nitrogen fertilizers in the lands of the region should be considered.Water quality assessment is one of the most important research and implementation issues in the world. Load continuity curve is a method that can achieve favorable results with less data in the field of determining the quality conditions of the river and its influencing factors. In this research, LDC curves were used to determine the pollutant sources affecting the Tajen River. First, with the help of probability distribution functions and stream discharge information (18 years), the best curves of flow continuity in two hydrometric stations including Rig Cheshme and Kordkhil were drawn, and then the curves of the maximum allowable load of nitrate pollutant for two agricultural uses and water ecosystem in the cultivation and non-cultivation seasons were created. Then the LDC curves were drawn for the 8-year period from 1390-91 to 1397-98.The results showed that in the area of Rig Cheshme station, most of the non-point sources affect the nitrate pollution of the river, however in Kordkhel station, due to the occasional increase in the amount of nitrate pollution for minimum discharges in the cultivation seasons, it was determined that non-point sources are the cause of this increase. The results of this study showed the favorable ability of load continuity curves in determining the origin of pollutant load. On the other hand, with the investigations done and according to the quality conditions of the river from Kordakhil station to the mouth of the Tajen river, it was found that the river is more polluted than the permissible limit near the mouth during the cultivation seasons. Therefore, the revision of the calculations of releasing the flow from the Shahid Rajaei dam, the optimal water consumption in the river, and the management of the application of nitrogen fertilizers in the lands of the region should be considered.

Labyrinth weirs are often a desirable design option to regulate upstream water elevations and increase flow capacity and the gate structures have some advantages including passing the floating substances and the sediments in using combined weir-gate structure. But, it can be difficult to design due to the complex flow characteristics of a labyrinth weir-gate. A labyrinth weir could be described as a continuous and broken weir plan in a trapezoidal or triangular form. Thus, for a fixed width, labyrinth weirs have a longer crest distance when compared to linear one. Most weirs create a relatively static water zone in their upstream, which can be the site for sedimentation and waste materials, which is a disadvantage of these structures. Because of the sediments deposition in upstream of the weirs the flow conditions change and the accuracy of the presented relationships is reduced. Although numerous methods of design have been published for labyrinth weirs, there is insufficient design information available about the combined models of Labyrinth weir-gate.

This study was conducted to improve labyrinth weir -gate design and analyses techniques using physical-model-based data sets. The experiments were conducted in a metal and glass flume with a rectangular cross-section. The flume was 0.25 m wide, 0.5 m deep, and 10 m long. In each test the upstream subcritical depth was measured using point gauges of 0.1 mm accuracy The location for measuring the total head of the water upstream of the weir is a horizontal distance of three to four times the maximum water head on the crest of the weir. The discharge was measured with a triangle sharp weir placed at the end of the flume. The discharge-head relationship (Q-h) for triangular weir in experiments is as Q=0.6918 h2.5. The trapezoidal labyrinth weir-gate models was installed at the distance of 3 m of the beginning of flume. The base material roughness was made of natural sand with a mean diameter of 3 mm. In this research, experimental study of combined flow trapezoidal labyrinth weir-gate with one cycle has done for three sidewall angles of 15, 20 and 25 degrees, three gate openings 2, 4 and 6 cm and the weir height of 14 cm in a rectangular channel. According to the effective parameters of the combined models including sidewall angel (), gate opening (a) and the hydraulic head (Ht), the discharge coefficient has evaluated. By applying the Buckingham π theorem an equation was obtained. The discharge coefficient of trapezoidal labyrinth weir-gate can be expressed as a function of the variables of Fr,Re,We,H_t/P,L/H_t ,H_t/a,ϕ,α. In this study, the depth of water measured on the weir crest is at least 3 cm, so the effect of surface tension on the weir (We) is negligible. The effect of dynamic viscosity on the hydraulic behavior of the flow can be ignored. Therefore, the Reynolds number (Re) can be removed.

The results show that the discharge coefficient decreases with increasing the ratio of Ht/P for both smooth and rough beds and it reaches a constant discharge coefficient for Ht/P >0.6. According to the effective parameters of the combined models, the discharge coefficient has obtained averagely in the range of 0.61-0.75. The discharge coefficient of the combined flow increases with increasing angle of the weir. The increase in discharge coefficient is due to the decrease in the length of weir which decreases the flow mixing. Also for a specified angle, the discharge coefficient increases with increasing of L/ Ht then gets a constant value. The effects of artificial roughness on discharge capacity are also presented. It can be shown that the discharge coefficient increases in rough bed condition compared to the smooth bed condition. The present test data and those of Crookston (2010) were compared and it can be seen that the discharge coefficient in combined flow trapezoidal labyrinth weir-gate is more than the discharge coefficient of the trapezoidal labyrinth weir (without gate) in Crookston (2010) investigation.

The discharge coefficient of trapezoidal labyrinth weir-gate has the highest value for weirs with a sidewall angle of 25° and gate opening of 6 cm in rough bed condition about 17% more than the smooth bed. Among the different experimental models with a sidewall angle of 25, the labyrinth weir-gate in the rough bed condition has the highest discharge coefficient (approximately 0.93) compared to the smooth bed (approximately 0.61).