JWSS - Journal of Water and Soil Science

The investigation of local scour below hydraulic structures is so complex that makes it difficult to establish a general model to provide an accurate estimation for the local scour dimension. During the last decades, Data Driven Methods (DDM) have  been used extensively in the modeling and prediction of unknown or complex behaviors of systems One of these methods is Group Method of Data Handling (GMDH), that is a self-organization approach and increasingly produces a  complex model during the performance evaluation of  the input and output data sets. So, the objective of this study was to investigate the potential of the GMDH method in the accurate estimation of local scouring geometry (maximum scour depth, the distance of maximum local scour depth till Ski-jump bucket and length of local scour) below the Siphon spillway with Ski-jump bucket energy dissipaters for a set of experimental data. 80% of data set was used for the training period and the remaining data set was used for the test period. The average values of MSRE, MPRE, CE and RB for the nonlinear second order transfer function (FUNC1) were calculated to be 0.92, 0.02, 8.74, -0.01; also, for the nonlinear first order transfer function (FUNC2), they were 0.85, 0.02, 10.43 and -0.02, respectively. The results indicated that the performance of FUNC1 was better than FUNC2. Also, the value of the coefficient of determination (R2) for the estimation of local scour dimension using different methods such as s linear regression, nonlinear regression and ANN indicated the high performance of the developed model of GMDH in the accurate estimation for local scour dimensions.

In the recent decades, due to the development of the pressurized irrigation systems, the relationship between the water and energy has been extended more than ever. So, according to problems due to the water shortage, energy saving is considered as one of the most important challenges in the agriculture section. In this study, by considering the capabilities of the pumping systems, variable speed pumps have been examined in an agro-industrial region of Ashrafiyeh (Qazvin province, Iran) with an area of 85 ha. The energy consumption during the ten-year operation was analyzed in the five operation scenarios by the MATLAB/SIMULINK software. The results showed that the consumed electrical energy by using variable speed pumps was approximately decreased up to about 18 percent, as compared to the fixed speed pumps. The results of the evaluation of the consumed energy showed that the current operation circumstance increased energy losses up to about 60 percent, as compared to the other operation methods. The results also revealed that the overall energy efficiency for the current operation circumstance was 52 percent (78 percent of Nebraska Performance Criteria).

The aim of this study was to evaluate the effect of increasing DEM spatial resolution on the assessment of the morphometric characteristics of waterways, as well as analysis and modeling of it by using RS and GIS techniques. In this study, which was carried out in the south of Darab city DEM 90 m (as one of the most usable data in waterway modeling), increase spatial resolution of DEM attraction algorithm in neighboring pixels with two models including: touching and quadrant neighboring models to estimate the value of sub-pixels. After manufacturing output images for sub pixels in 2, 3 and 4 scales with different neighborhoods, the best scale with the most appropriate type of neighborhood was determined using ground control points (270 points); then, the values of RMSE were calculated for them. The results showed that with using the Attraction model, the accuracy of the output of images was improved and the spatial resolution of them was increased. Among scales with different neighborhoods, 3 scales and quadrant neighboring model exhibited the most accuracy by the lowest value of RMSE for the DEM 90 meter. Evaluation of waterways morphometric features showed that DEM extracted from attraction algorithm had more ability and accuracy in waterways extraction, Extraction of morphometric complications, and information in the study area.

Quantitative description of the spatial variability of soil hydraulic characteristics is crucial for planning, management and the optimum application. Field measurement of infiltration is very expensive, time-consuming and laborious. Soil structure also important effects on water infiltration in the soil. The objectives of this study were to determine the spatial variability of water infiltration, to select the most appropriate infiltration model, to calculate the parameters of relevant models, and to quantify the soil structure by using the fractal geometry. Infiltration parameters were estimated by using some physical soil properties, as well as fractal parameters, in this research. To achieve these purposes, 161 sites were selected and their infiltration was measured by using the constant head double-ring infiltrometers method in a systematic array of 500*500 m. The observed infiltration data from all examined sites were fitted to three selected infiltration models. Soil bulk density (BD), soil water content, soil particle size distribution, soil aggregate size distribution (ASD), organic carbon content (OC), saturation percentage (SP), soil pH and electrical conductivity (EC) were also measured in all 161 sites. For the quantitative assessment of soil structure, the aggregate size distribution, fractal parameters of the Rieu and Sposito model as well as the mean weight diameters (MWD) and geometric mean diameter (GMD) were also obtained. The obtained results indicated that the infiltration rates of the studied areas had generally low basic infiltration rates (1.1-31.1 cm hr^-1) for most sites with the average of 6.69 cm hr^-1. According to all obtained results and based on the least-square method, the Philip model was selected as the best performing model to account for infiltration. The aggregate size distribution demonstrated a fractal behavior, and the infiltration parameters could be significantly correlated with the fractal parameters and other soil physical properties.

Due to the growing population, crop production is one of the essential needs of the society. Since soil and water salinity can have a great impact on the crop yield loss; so, the appropriate irrigation method can be applied to reduce these effects. In this study, the system dynamics model was developed using VENSIM. The model simulated the effect of salinity and water stress on the crop yield, moisture and salinity of the root zone. In order to calibrate and validate the model results, 9 treatments data were collected from the Right Abshar Irrigation Network, on the Zayandehrud basin. After statistical analysis and calculation of RMSE index and the standard error, the fit between the measured and simulated crop yield, the moisture and salinity of root zone was calculated. The average of these indexes for all treatments was 2776.98 kg/ha and 0.07 for crop yield, 0.026 and 0.09 for soil moisture and final, 0.54 dS/m and 0.08 for the salinity of root zone, respectively. The results showed that the model could be calibrated accurately and completely in estimating the crop yield with the reasonable accuracy.

Seepage losses and poor operational activities are the two main source of water losses throughout the agricultural water conveyance and distribution systems in irrigation districts. This study aims to investigate the performances of two strategies of “canal lining” and employing the “Canal Automation” in order to reduce the losses mentioned above. The investigation was carried out on a couple of main canal reaches of Moghan Irrigation Districts. Two numerical models were simulated by Seep/w software to compare the seepage rate between the canal with and without concrete lining. The results reveal that the ability of concrete lining to reduce seepage losses along the canal is about 10%. Performance assessment of the “Canal Automation” strategy to minimize operational losses within the main canal was carried out employing Model Predictive Control (MPC). The results of the latter strategy indicate that employing the MPC not only reduces the operational losses along the canal by 15% but also improves the operation of the main canal so that the minimum efficiency and adequacy performance indicator was obtained 100% and 83% respectively. Therefore; due to Executive considerations and financial constraints in the same cases, the potential of each of the two strategies can be considered to reduce the conveyance and distribution losses and ultimately choose the most suitable option.

Experimental and numerical study of scouring pattern on the direct and polo-shaped groynes have been investigated in this paper. In this study, direct and polo-shaped groynes models with a length of 0.12 meter have been used in discharges of 10.5, 15, 20 liters per second in a direct flume. The results showed that the maximum scour depth formed around the groyne head of direct and polo-shaped types has increased with augmentation of flow discharge, which was 0.095 and 0.104 meter in the case of 20 L/s discharge respectively. Also, the width of scour hole was 2.25 and 2 times of effective length of the groyne in direct and polo-shaped groynes respectively. In this regards, maximum scour depth around the head of groyne was seen 0.87 and 0.79 times of the effective length of the groyne. Sand form located at downstream of the direct groyne at the distance of 0.09 and 0.15 meters from the side wall of direct groyne was stretched and extended to about 1.3 times of the channel width as well. While the length of the sand form for direct groyne was 1.15 times of the channel width. Overall, the dimensions of the scour hole around the polo-shaped groyne, was less than the direct groyne. In addition to understanding the hydraulic behaviour around the groyne, Flow3D software was used. Statistical survey of the results obtained by experimental and numerical models attested that the relative error of the numerical model could be about 20%, which shows an appropriate performance of using Flow3D for predicting the maximum scour depth.

In this research, by using FLOW3D, the performance of non-linear (arced) piano key (PKW-NL) in plan and linear piano key weir (PKW-L), with equal length of weir, was compared. Results showed that nonlinearity of the weir caused 20% increase in the discharge coefficient. Investigating the velocity contours for these two weir models also showed that maximum velocity within the PKW-NL weir structure is about 30% lower than the PKW-L weir. Also, the performance of non-linear piano key weir was evaluated under inward (PKW-IC) and outward (PKW-OC) curvatures to the channel. Results showed that in the case of PKW-IC weir, the discharge coefficient was increased by 8% as compared to the PKW-OC weir. Investigating the pressure contours for these two weir models also shows that the average pressure within the PKW-IC weir structure is about 5% higher than the PKW-OC weir. This increase in pressure leads to a decrease in the speed and better distribution of flow over the weir keys.

This study was conducted to evaluate AquaCrop for the simulation of potato yield and water use efficiency (WUE) under different water stress values at five levels (E0, E1, E2, E3 and E4, indicating 100, 85, 70, 50 and 30 percent of crop water needed, respectively) in three times during growth cycles (T1, T2, and T3, indicating 50, 100, and 150 days after sowing, respectively). The results showed that AquaCrop had overestimated and underestimated error for the simulation of yield and WUE, respectively. Based on RMSE and NRMSE values, the errors for yield and WUE were acceptable. The maximum and minimum error were also 0.3 (E1T3) and 3.15 (E1T2), respectively. The results obtained for WUE showed that the maximum and minimum were 0.53 (E3T2) and 0.03 (E4T2), respectively. The average differences between simulated and observed results (ADSO) of WUE for E1, E2, E3 and E4 were 0.24, 0.25, 0.19, and 0.44 ton.ha^-1, respectively; the ADSO of yield for T1, T2, and T3 was 0.19, 0.36, and 0.22 ton.ha^-1, respectively. Therefore, AquaCrop showed a high error for WUE when water stress was increased and crop was in its initial crop growth.

In this study, the discharge coefficient of sharp-crested weirs located on circular channels was modeled using the ANFIS and ANFIS-Firefly (ANFIS-FA) algorithm. Also, the Monte Carlo simulations (MCs) were used to enhance the compatibilities of the soft computing models. However, the k-fold cross validation method (k=5) was used to validate the numerical models. According to the input parameters, four models of ANFIS and ANFIS-FA were introduced. Analyzing the numerical results showed that the superior model simulated the discharge coefficient as a function of the Froude number (Fr) and the ratio of flow depth over weir crest to the weir crest height) h/P(. The values of the mean absolute relative error (MARE), root mean square error (RMSE) and correlation coefficient (R) for the superior model were calculated 0.001, 0.002 and 0.999, respectively. However, the maximum error value for this study was less than 2%. 

Knowledge about the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. During the last two decades, the utilization of data mining approaches in spatial modeling of SOC using machine learning algorithms have been widely taken into consideration. The essential step in applying these methods is to determine the environmental predictors of SOC optimally. This research was carried out for modeling and digital mapping of surface SOC aided by soil properties ie., silt, clay, sand, calcium carbonate equivalent percentage, mean weight diameter (MWD) of aggregate, and pH by machine learning methods. In order to evaluate the accuracy of random forest (RF), cubist, partial least squares regression, multivariate linear regression, and ordinary kriging models for predicting surface SOC in 141 selected samples from 0-30 cm in 680 hectares of agricultural land in Khorramabad plain. The sensitivity analysis showed that silt (%), calcium carbonate equivalent, and MWD are the most important driving factors on spatial variability of SOC, respectively. Also, the comparison of different SOC prediction models, demonstrated that the RF model with a coefficient of determination (R2) and root mean square error (RMSE) of 0.75 and 0.25%, respectively, had the best performance rather than other models in the study area. Generally, nonlinear models rather than linear ones showed higher accuracy in modeling the spatial variability of SOC.

In the present study, a model was developed using a system dynamics approach to simulate and optimize the profitability of crops of the Jofeyr (Isargaran) Irrigation and Drainage Network located in Khuzestan Province. To validate the results, the statistical indicators of root mean square error (RMSE), standard error (SE), mean biased error (MBE), and determination coefficient (R²) were used. To validate the simulation results of the benefit-cost ratio, the values of these indicators were obtained 0.25, 0.19, 0.005, and 0.96, respectively. Then, to determine the optimal cultivated area of the network and increase the profitability, the cropping pattern was determined both non-stepwise and stepwise in 2013 to 2017 cropping years. In the non-stepwise, the cultivated area of each crop changed from zero to 2 times of current situation. In stepwise, due to social and cultural conditions of inhabitants, this change was slow and 10% of the current situation every year. The analysis of the results showed the success of the model in optimizing and achieving the desired goals and the total benefit-cost ratio increased in all years both non-stepwise and stepwise. For example, in 2017 compared to 2016, production costs decreased by 7.1 percent and sales prices increased by 5.8 percent, and increased the benefit-cost in 2017 compared to the previous year. The results showed that the present model has good accuracy in simulating and optimizing the irrigation network, its cropping pattern, and defining other scenarios.

The impact of different management options on the region and the existing conditions can be evaluated with minimal cost and time to select the most practical case using various tools including mathematical models. In this study, the SWAT hydrological model was performed from 2009 to 2019 using climatic, hydrological, and hydrometric data in the Malayer catchment, and the final model was validated by SWAT-CUP. To reduce the amount of uncertainty in the input parameters to the MODFLOW model, using the values of surface recharge from the implementation of the SWAT hydrological model, quantitative modeling of Malayer aquifer was performed more reliably in GMS software by using MODFLOW model. After modeling the study area in the 2009-2018 period and calibrating the model in the years from 2018 to 2019, the mean values of absolute error (MAE) were 0.35-0.65 m, and root means square error (RMSE) was 0.62-0.94 m, which seems acceptable considering computational and observational heads equal to 1650 m. Results of water level changes in observation wells located in the Malayer region indicate that the groundwater level in the aquifer has decreased by an average value of 9.7 m in the 10-year study period.

The land use change as well as changes in climatic parameters such as temperature increase affect many natural processes such as soil erosion and sediment production, floods, and degradation of physical and chemical properties of soil. Therefore, it is necessary to pay attention to different aspects of the effect of these changes in studies and macro decisions of the country. In the present study, the SWAT conceptual model was used to test and analyze the existing scenarios in the Marvast basin. After calibrating the model, the two scenarios were tested. The first scenario is in the field of agricultural management and conversion of gardens to agricultural lands and the second scenario is a 0.5-degree increase in temperature by assuming other conditions are constant. The calibration and validation results of the model with the Nash-Sutcliffe test showed 0.66 and 0.68 respectively, which indicate the acceptable performance of the model in the study area. Then, the results of using two scenarios of land use change and heating, especially in recent years showed the effect of 30 percent of the climate scenario on the increase of flooding in the basin. The scenario of changing the use of garden lands to agriculture in two cases of 20% and 50% change of use of 10% and 12% was added to the flooding of the basin. The results indicate that in similar areas of the study area which is located in a dry climate zone, a possible increase in temperature can have a significant effect on flooding in the basin. However, the indirect impact of the human factor in increasing greenhouse gases and flooding in the basin should not be ignored.

The agricultural sector as one of the most important sectors of water consumption has great importance for the sustainability of the country's water resources systems. The objective of this study was to estimate the river water abstraction (RWA) for agricultural consumption in the study area of Nobaran in the Namak Lake basin. The RWA was estimated using variables related to morphological, hydrological, and land use factors, as well as a combination of their variables collected through field sampling. Data mining methods such as adaptive-network-based fuzzy inference systems (ANFIS), group method of data handling (GMDH), radial basis function (RBF), and regression trees (Rtree) were also used to estimate the RWA variables. In the current study, the GMDH₂₄ model with a combined scenario including the variables of river width, river depth, minimum flow, maximum flow, average flow, crop, and the garden cultivated area was adopted as the best model to estimate the RWA variable. The RMSE value for the combined scenario of the GMDH₂₄ model was found to be 0.046 for estimating RWA in the Nobaran study area. The results showed that the performance of the GMDH₂₄ model for estimating RWA for maximum values is very acceptable and promising. Therefore, modeling and identifying various variables that affect the optimal RWA rate for agricultural purposes fulfills the objectives of integrated water resources management (IWRM).

Introducing reliable regional models to predict the maximum discharge of floods using characteristics of sub-basins has special importance in terms of flood management and designing hydraulic structures in basins that have no hydrometric station. The present study has tried to provide appropriate regional flood models using generalized linear models (GLMs) to estimate 2-, 10-, 50-, and 100-year maximum daily discharges of 62 sub-basins in Great-Karoon and Karkhe basins. According to the results, the sub-basins were categorized into four sub-regions based on some physiographic and climatic characteristics of the study sub-basins. The results showed that regional flood modeling was successful in all sub-regions except sub-region II, which includes very large basins (A̅≈17300 km2). The adjusted R² of the best models in sub-regions I, III, and IV were estimated at around 82.4, 91.3, and 90.6 percent, and these models have a relative error (RRMSE) of around 9.5, 9.23, and 6.7 percent, respectively. Also, it was found that more frequent floods with 2- and 10-year return periods are influenced by properties such as basin’s length, perimeter, and area, while rare floods with 50- and 100-year return periods are mostly influenced by the river systems characteristics such as the main river length, total lengths of the river system, and slope of the main river. According to the research, it can be stated that the behavior of maximum daily discharges in the study area is extremely influenced by the different climatic and physiographic characteristics of the watersheds. Therefore, the maximum daily discharges can be estimated accurately at ungauged sites by appropriate modeling in gauged catchments.