مجله تحقیقات آب و خاک ایران
سال پنجاه و پنجم شماره 4 (پیاپی 100، تیر 1403)

Simulation of runoff and hydrograph production is widely used in analyzing the behavior of basin against precipitation, calculating the volume and peak of floods, the amount of losses and the possibility of designing the dimensions of structures. HEC-HMS is one of the most common simulator models In this study, with the aim of analyzing the sensitivity of flood hydrograph routing parameters in the Merk River basin, first, the physiographic characteristics of the basin were constructed with the HEC-GeoHMS plugin in the Arc GIS environment and the DEM map of the area and they were called in the HEC-HMS model. then by entering the three flood event data and its corresponding precipitation, computational hydrograph is created.  For calibration, at first by trial and error, the model was placed in a suitable range which can be optimized using differential evolution algorithm. In optimizing the target function to make the average at least of the sum of squares of error, the K and X Muskingum parameters, which are particularly sensitive to hydrograph production, placed the model optimization and its performance in the excellent category so that for the event of November 2015 floods and a stop of 50 iteration (NSE=0.871, PBias=25.52, RMSE=0.4, NRMSE=2.63), for the event of March 2016 floods (NSE=0.731, PBias=28.82, RMSE=0.5, NRMSE=1.01) and in February 2020 (NSE=0.834, PBias=7.96, RMSE=0.4, NRMSE=0.95) which indicate an excellent performance of the model after optimization of the Muskingum coefficients by differential evolution algorithm (DE).

In order to control and minimize the damaging impacts of floods, flood modeling or simulation is a fundamental solution. Identifying effective models for this purpose is crucial in watershed management. This study evaluates the accuracy of support vector machine models combined with the support vector machine (SVM), Grasshopper algorithm (SVM-GOA) and least square support vector machine (LS-SVM) in simulating the flood peak discharge of Poldokhtar station in the Karkheh basin. For this study, 74 flood events from 2009 to 2016 at the Poldokhtar station and data from 13 daily rainfall stations in the upstream area for the same period were utilized. Subsequently, 52 events were allocated for training, and 22 for validation. The comparison of results was conducted using three statistical indicators: Correlation coefficient (R2), Root mean square error (RMSE), Nash efficiency (Ns), and Standard error (SE). Additionally, uncertainty analysis was performed using two indexes: ARIL and POC. The results indicate the relative superiority of the LS-SVM model with SE=0.407, RMSE=110.16, NS= 0.91 and R2=0.92 compared to the SVM model with SE=0.5, RMSE=137.70, NS= 0.87 and R2=0.88 and SVM-GOA model with SE=0.519, RMSE=144.53, NS= 0.83  and R2=0.9. The study's overall conclusion is that the LS-SVM model is more accurate, faster, and easier to implement compared to the SVM and SVM-GOA models. As a result, it can be confidently preferred over the SVM and SVM-GOA models due to its significant advantages. The research emphasizes the critical importance of precise flood modeling and simulation in watershed management for mitigating the destructive impact of floods.

In this study, the effect of altitude on climate and soil properties of forest soils in the Talesh region of Gilan province, Iran was investigated. Soil samples were collected from four altitudinal classes (500-1000, 1000-1500, 1500-2000, and 2000-2500 m) and soil properties and nutrient contents were measured. Climate data were also collected from the TerraClimate database. The results showed that with increasing altitude, precipitation increased and temperature decreased. The rate of increase in precipitation was 536.50 mm and the rate of decrease in temperature was 7.40 °C per 1000 m increase in altitude. Evapotranspiration also decreased with decreasing temperature at higher altitudes. Soils at higher altitudes had higher organic matter, total nitrogen, soil aggregate stability, and water holding capacity. However, available nutrients (K, S, P) were lower due to greater leaching and slower decomposition of organic matter at higher altitudes. The decrease in these nutrients was 15.18, 7.89, and 42.14%, respectively. Soil pH, bulk density, and salinity also decreased with increasing altitude. The findings of this study showed that although soils at higher altitudes have higher organic carbon content and water holding capacity, which can improve soil quality and forest ecosystem performance, they have lower available nutrients due to greater leaching. This can lead to nutrient depletion and acidification of these soils. Therefore, proper management of these soils, such as fertilization and liming, is essential to maintain ecosystem health.

The aim of this research is to predict fluctuations in the equivalent thickness of groundwater using GRACE satellite data and modeling it using artificial intelligence hybrid models. The study area of this research is the basin area of Lake Urmia located in the northwest of Iran. For this purpose, 180 GRACE satellite data between April 2002 and March 2017 were used. The output of GRACE satellites includes 6 pixels located on the selected watershed, of which 2 points that overlapped the most with the watershed area were selected for modeling with artificial intelligence tools. The GA-ANN, ICA-ANN and PSO-ANN hybrid models were used for this purpose. The results showed that the output of the ICA-ANN model had the best fit with the observation data with a correlation coefficient equal to 0.915 and 0.942 in the two selected pixels 2 and 5 in the test phase, and the results of this model had the best and closest distribution of points. Considering the importance of knowing the changes in the equivalent thickness of groundwater as one of the most important parameters of the water budget, the artificial intelligence models used in this research can be recommended, especially for areas without basic statistics or in situations where it is not possible to use mathematical models. Without the need for complex relationships and equations to investigate the effect of surface and groundwater interaction and only based on satellite data, the equivalent thickness of groundwater can be predicted in the studied plain in dry and wet periods with great accuracy.

Powdered rock phosphate may have the potential of soluble phosphate sorption because its particles are small.  Phosphorus (P) sorption-desorption behavior and sorption kinetics were studied on four different fine powdered phosphate rocks (PPR), from Esfordi, Chadormalu and Yasuj mines. Sorption isotherms were evaluated by equilibrating 1 g PPR with 20 ml CaCl2- 0.01 M containing 10, 50, 100, 200, 400, 600, 800 and 1000 mg P L-1, as KH2PO4 in duplicates. Consequently, P release was studied by equilibrating the samples with 20 ml 0.01 M CaCl2. FTIR analysis was conducted on the blank samples, and those equilibrated with 1000 mg P L-1. Kinetics of phosphate sorption on PPRs was evaluated with two initial P concentrations (50 and 100 mg P L-1) and contact times of 1, 2, 4, 8, 16, 24, and 48 h with 0.01 M CaCl2 as the background solution in duplicate. Results showed that the least P sorption was around 150, and the maximum P sorption was 12000 mg kg-1 PPRs. The highest P release was around 15% of sorbed P, and releasable P was reduced to 2-3% of sorbrd P with the increase of P sorption. P sorption data showed a good fit with Freundlich and Langmuir equations. The kinetic of P sorption showed a fast reaction that rapidly diminished soluble P. Results of the present study suggest that PPRs could reduce soluble P, which is better to be considered at different usage of PPR such as incorporation with organic wastes and its biosolubilization.

One of the ways to prevent creating negative pressure and cavitation in spillways is to introduce air into the flow over the spillways. Understanding the distribution of air concentration variations along the spillway is of significant importance for estimating the aeration level. This study explores the application of GPR and SVM molels in predicting air concentration. To achieve this, a dataset of 2268 laboratory experiments obtained from hydraulic models of chute spillways was utilized in the modeling process. Various input models were defined based on different combinations of measured parameters. The results demonstrate the high capability of both methods in estimating the required air concentration over the spillway. In predicting air concentration in the chute spillway under artificial aeration conditions, flow discharge (QW), longitudinal distance ratio from the end of the deflector to the channel width (L/W), and depth ratio (perpendicular to the spillway) to channel width (Y/W) significantly influenced the outcomes. Statistical indices, including R, DC, and RMSE for this case were 0.9214, 0.8451, and 1.008, respectively, in the GPR, and 0.9333, 0.8662, and 0.937 in the SVM. For scenarios without artificial aeration, the model with input parameters QW, L/W, Y/W, and ΔP (pressure difference between atmospheric pressure and the pressure under the jet) achieved the best performance in the GPR method with values of R=0.9222, DC=0.8644, and RMSE=0.914. In the SVM, the same model with values of 0.87, 0.7543, and 0.123 for R, DC, and RMSE, respectively, was selected as the superior model.

Using simulation models is a key strategy in agricultural water management and an effective way to predict the impact of irrigation management and water quality on crop yield. This study aimed to evaluate the performance of the SWAP and AquaCrop models in simulating the growth and biomass production of three maize varieties under the conditions of using saline water for irrigation with a drip irrigation system at the research farm of Tehran University of Agriculture and Natural Resources Campus. In order to calibrate and validate the models, field data obtained from a factorial experiment with two factors of maize variety (SC704, 400, and 260) and irrigation water salinity (0.7, 3, and 5 dS/m) were used. In the validation stage of the AquaCrop model, the R2, RMSE, and NRMSE statistics for the canopy cover (CC) showed a high level of agreement between the measured and simulated data, with values of 0.953, 5.69, and 8% respectively. For the SWAP model, the calculated statistics for the leaf area index (LAI) were 0.477, 1.610, and 54.2%, respectively. Despite the poor performance of the SWAP model in estimating LAI, both the SWAP and AquaCrop models effectively simulated the biomass of maize cultivars under different treatments. In the calibration and validation stage, RMSE and NRMSE of both models were less than 0.5 ton/ha, with 3% (calibration) and 1 ton/ha, 7% (validation), respectively. In general, both models can be used in various studies for different maize cultivars under irrigation water and soil salinity conditions.

Considering the importance of knowing and awareness of the watersheds water balance status, and analyzing the hydrological behavior of watersheds for planning and implementing water-related projects, the need to use new technologies in predicting water balance components is more evident than ever. Based on this, in the Kashkan basin by utilizing the TOPKAPI-X hydrological model, the water balance components of the basin were simulated according to the cellular network design. Digital maps of the basin, land use, outlet point, soil texture, elevation, and continuous time series of temperature, precipitation, and discharge in the daily time step are the main inputs of the model. The model in each cell network balances the water balance of the entire period. Model calibration was done for the 15 years of the statistical period (1999 to 2014) and model validation for the 6-year period (2014 to 2020). The results showed that 27.02 and 28.43 percent of the total precipitation of the Kashkan basin was discharged from the basin as total runoff (respectively for calibration and validation periods), which is consistent with the observation data at the outlet hydrometry station. Next, to evaluate the efficiency of the model, the simulated values in both statistical periods were compared to the observational discharge. Statistical methods such as the Nash-Sutcliffe evaluation criteria showed that the TOPKAPI-X model predicted the water balance components such as actual and potential evapotranspiration, infiltration, and the amount of runoff, especially the total runoff, in this basin with relatively good accuracy (coefficient above 60%).

The present study investigated the effect of the combination of zeolite, mycorrhizal inoculation, and phosphorus fertilizer on mung bean production in the year 2022-2023 in the climatic conditions of Isfahan City. The experiment was performed as a split-split plot as a basic randomized complete block design with three replications. The main factor included zeolite at three levels (zero, 3, and 6 ton/h), and the secondary factors included mycorrhizal inoculation (inoculation and non-inoculation) and phosphorus fertilizer at three levels (zero, 75 and 150 kg/h). The results showed that the main effects of mycorrhizal inoculation and phosphorus fertilizer application on all measured traits were significant. The interaction effect of zeolite and phosphorus fertilizer showed that the highest seed yield was observed in the treatment of six ton/h of zeolite and not use or using 75 kg/h of phosphorus fertilizer at the rate of 2920.6 and 2895.67 kg/h, respectively, and the lowest in the absence of zeolite. The zeolite and phosphorus fertilizer consumption was obtained at the rate of 2063.36 kg/h. The results showed that the highest biological yield was observed in the treatment of six ton/h of zeolite and mycorrhiza + 75 kg/h of phosphorus fertilizer at the rate of 8127 kg/h, and the lowest in the case of no zeolite consumption, no mycorrhiza use and no use of phosphorus fertilizer. A total of 3893 kg/h was obtained. In general, combining or using zeolite, mycorrhizal, and phosphorus fertilizers in mung bean cultivation has led to improved growth, increased production, and improved product quality.

The wetting drying cycles, through the creation of Swelling Shrinkage sequences in soil aggregates, lead to changes in the soil's microstructure. In this study, attempts were made to investigate changes in the geometric properties of loamy sand and silty clay soils by applying moisture cycles in different treatment conditions, including amendmentsand degradations. The hypothesis of this research is based on the premise that the presence of different treatment intensities of the effects of wetting drying cycles affects the soil's microstructure from the perspective of soil aggregates. Using ImageJ software, image processing was performed on 2D and 3D images acquired from soil blocks. In addition to soil aggregate volume and surface area, properties such as sphericity and flatness coefficients were estimated and used for the classification of soil aggregates. For statistical analysis and chart plotting, Orange 3 and Excel 2016 software were used. The results indicated that treatments such as calcium carbonate, cations, and organic matter increased the coefficient of soil aggregates elongation, while degradation treatments led to an increase in the coefficient of soil aggregates flatness in both studied soil textures. Geometric classification of soil aggregates revealed that a small portion of loamy sand soil aggregates were categorized as elongated soil aggregates, while more than half of them fell into the category of flatted soil aggregates. In silty clay soil samples, a uniform distribution of elongated, bladed, and flatted soil aggregates was observed. However, none of the studied treatments resulted in soil aggregates falling into the category of compacted soil. Considering the direct impact of soil aggregate shape on the hydraulic conductivity of soils, the method employed in this research can effectively determine the microstructural status of soils from the perspective of soil aggregates.