Showing 25 results for Halil
B Bakhtiari, A.m Liaghat, A Khalili, M.j Kjanjani,
Volume 13, Issue 50 (winter 2010)
Abstract
In this study, the Penman-Monteith methods proposed by the Food and Agriculture Organization (FAO-56) and American Society of Civil Engineers (ASCE) were used for hourly ETo estimation under the semiarid climate of Kerman, Iran. Hourly ETo estimations obtained from the proposed methods were compared with measured ETo values by using a large weighing electronic lysimeter during April to September 2005 (totally 3352 hourly ETo data cases). Simple linear regression and statistical factors such as root mean square error and index of agreement were used for estimated and observed value comparison. The average of measured and estimated hourly ETo values using these methods for integrated data were 0.28 and 0.23 mm hr-1, respectively, which means that average estimated ETo values were approximately 21 percent less than the measured ETo values. This analysis was also performed for hourly data of each month during the study period. The results showed that FAO-56 Penman-Monteith underestimated ETo values by 18.4, 19.3, 26.3, 20.4, 21.4 and 22.1 percent for April to September, respectively, when compared with the measured values. Similarly, the ASCE Penman-Monteith underestimated ETo values by 17, 19.6, 18.4, 18.2, 19.7 and 20.9 percent for the same period, respectively, when compared with the lysimetric data. Finally, a set of regression equation for transformation of the estimated hourly data into the measured hourly ETo values has been presented for each month.
Sh. Ghorbani Dashtaki, S. Dehghani Baniani, H. Khodaverdiloo, J. Mohammadi, B. Khalilmoghaddam,
Volume 16, Issue 60 (Summer 2012)
Abstract
Saturated hydraulic conductivity (Kfs) and macroscopic capillary length of soil pores are important hydraulic properties for water flow and solute transport modeling. Measuring these parameters is tedious, time consuming and expensive. One way is using indirect methods such as Pedotransfer functions (PTFs). The objective of this research was to develop some PTFs for estimating saturated hydraulic conductivity and inverse of macroscopic capillary length parameters (*). Therefore, the coefficients, Kfs and * from 60 points of Azadegan plain in Shahrekord were measured using single ring and multiple constant head method. Also, some of the readily available soil parameters from the two first pedogenic layers of the soils were obtained. Then, the desired PTFs were developed using stepwise multiple linear regression. The accuracy and reliability of the derived PTFs were evaluated using root mean square error (RMSE), mean error (ME), relative error (RE) and Pearson correlation coefficient (r). The highest correlation coefficients of 0.92 and 0.72 were found between Kfs-bulk density and *-bulk density, respectively. There was no significant correlation between soil particle size distribution and Kfs and *. This can be related to the fact that most of the soil samples were similar in texture and macro pores. The most efficient PTFs in predicting Kfs and * could explain 85 and 66 percent of the variability of these parameters, respectively. All the derived PTFs underestimated the Kfs and * parameters.
E. Habibi, M. A. Asoodar, B. Khalil Mogaddam,
Volume 18, Issue 67 (Spring 2014)
Abstract
Extensive tillage leads to the degradation of soil structure and aggregate stability. The effects of three different tillage implements including (combination tillage, chisel packer and disk), three levels of soil water content including (0.5, 0.7 and 0.9 plastic limit), three working speeds including (6, 8 and 10 km/hr) and two soil textures including (loam and silty clay loam soils) were studied. The study was carried out in Khuzestan Ramin Agriculture and Natural Resources University, in 2011. Soil aggregate mean weight diameter (MWD), geometric mean diameter (GMD) as determined by wet sieving method and soil surface roughness (SSR) were measured. Results showed that chisel packer with 0.744 mm MWD produced largest soil aggregates in both soils because of using narrow blade and shanks and small rake angles compared to other tillage treatments. Soil aggregate size was shown larger than other tillage treatments where chisel packer was employed but it was not suitable because of deep furrow shapes. Combination tillage was able to make a better soil condition to be used for seeding where the experiment was conducted.
F. Moradi, B. Khalilimoghadam, S. Jafari, S. Ghorbani Dashtaki,
Volume 18, Issue 69 (fall 2014)
Abstract
Soft computing techniques have been extensively studied and applied in the last three decades for scientific research and engineering computing. The purpose of this study was to investigate the abilities of multilayer perceptron neural network (MLP) and neuro-fuzzy (NF) techniques to estimate the soil-water retention curve (SWRC) from Khozestan sugarcane Agro-Industries data. Sensitivity analysis was used for determining the model inputs and appropriate data subset. Also, in this paper, the van Genuchten and Fredlund and xing models were used to predict SWRC. Measured soil variables included particle size distribution, organic matter, bulk density, calcium carbonate, sodium adsorption ratio, electrical conductivity, acidity, mean weight diameter, plastic and liquid limit, resistance of soil penetration, water saturation percentage and water content for matric potentials -33, -100, -500 and -1500 kPa. The results of this study in terms of various statistical indices indicated that both MLP and NF provide good predictions but the neural network provides better predictions than neuro-fuzzy model. For example, using MLP and NF models values of NMSE at prediction θs, θr, α, n and m in Fredlund and Xing equation corresponded to (0.059, 0.065), (0.154, 0.162), (0.109, 0.117), (0.129, 0.135) and (0.129, 0.145), respectively. Furthermore, α and n parameters at the first depth, and θr and α parameters at the second depth in Fredlund and Xing equation were estimated with higher accuracy compared with equivalent parameters in van Genuchten equation
B. Khalili Moghadam, Z. Ghorbani, E. Shahbazi,
Volume 18, Issue 69 (fall 2014)
Abstract
Salt with various kinds and contents is one of the most important factors affecting soil splash erosion rate. The aim of the present study was to evaluate various salinity and alkalinity levels on splash erosion rate and its components (upslope, down slope and total splash) in different slopes. A factorial experiment with three factors was conducted in a completely randomized design with three replications by a Multiple Splash Set (MSS). The treatments included splash erosion rate at 4 levels of salinity and alkalinity (EC: 2 dSm-1, SAR: 2، EC: 15, SAR: 24 ،EC: 56, SAR: 42، EC: 113, SAR: 47), two levels of rainfall intensity (2.5 and 3.5 mm.min-1) and 5% and 15% slope levels. The results showed that the organic carbon and mean weight diameter (MWD) decreased at higher levels of salinity and alkalinity. The effect of saline and sodic, slope and rainfall intensity levels on the splash erosion rate and its components was significant. Also, slope×saline and sodic, rainfall intensity×saline and sodic, slope×saline and sodic×rainfall intensity interaction treatment caused a significant increase in splash erosion rate and its components. It seems that splash erosion is increased in saline and sodic soils due to the reduction in OC and MWD
S. A. Banimahd, D. Khalili, A. A. Kamgar-Haghighi, Sh. Zand-Parsa,
Volume 18, Issue 70 (winter 2015)
Abstract
In the present research, the performances of six empirical models, i.e., simple threshold exceedance, fixed proportion exceedance, quadratic function of storage, power function of storage, cubic function of storage, and exponential function of storage were investigated for estimation of groundwater potential recharge in a semi-arid region. First, the FAO Dual Crop procedure was used to calibrate evaporation from bare soil during the occurrence of potential recharge period. Then, the empirical models were calibrated utilizing soil moisture and potential recharge data. For validation of empirical models, soil moisture and potential recharge were simultaneously estimated for an independent event. Results indicated that 5 of the six models (except for the simple threshold exceedance model) were able to estimate potential recharge with a reasonable accuracy, showing the maximum computed value of NRMSE (Normalized Root Mean Square Errors) of 24.4 percent. According to validation results, exponential, cubic, and power function models provided better estimation of potential recharge in comparison with the linear models. Also, all of the applied empirical models were able to simulate soil moisture during the recharge period with an acceptable accuracy. Finally, the exponential model with minimum NRMSE value for soil water simulation and also acceptable performance of potential recharge estimation was recommended for estimation of potential recharge in the study area.
B. Khalili Moghadam, M. Afyuni, A. Jalalian, K. C. Abbaspour, A. A. Dehghani,
Volume 19, Issue 71 (spring 2015)
Abstract
With the advent of advanced geographical informational systems (GIS) and remote sensing technologies in recent years, topographic (elevation, slope, and aspect) and vegetation attributes are routinely available from digital elevation models (DEMs) and normalized difference vegetation index (NDVI) at different spatial (watershed, regional) scales. This study explores the use of topographic and vegetation attributes in addition to soil attributes to develop pedotransfer functions (PTFs) for estimating soil saturated hydraulic conductivity in the rangeland of central Zagros. We investigated the use of artificial neural networks (ANNs) in estimating soil saturated hydraulic conductivity from measured particle size distribution, bulk density, topographic attributes, normalized difference vegetation index (NDVI), soil organic carbon (SOC), and CaCo3 in topsoil and subsoil horizon. Three neural networks structures were used and compared with conventional multiple linear regression analysis. The performances of the models were evaluated using spearman’s correlation coefficient (r) based on the observed and the estimated values and normalized mean square error (NMSE). Topographic and vegetation attributes were found to be the most sensitive variables to estimate soil saturated hydraulic conductivity in the rangeland of central Zagros. Improvements were achieved with neural network (r=0.87) models compared with the conventional multiple linear regression (MLR) model (r=0.69).
S. Abdoli, B. Khalili Moghadam, M. Rahnama,
Volume 19, Issue 71 (spring 2015)
Abstract
Quantitative measurement of aeolian dust may help properly monitor and control the wind erosion. The aim of this study was to evaluate the efficiencies of four aeolian dust samplers including the modified Wilson and Cooke sampler (MWAC), cyclone dust sampler with cone (CDSC), cyclone dust sampler (CDS), and marble dust collector (MDCO) in comparison with the big spring number eight sampler (BSNE) in different velocity rates and particles sizes. For this purpose, MWAC, MDCO, BSNE were simulated and CDSC and CDS were designed and constructed. The relative efficiencies of the CDSC, CDS, MWAC, and MDCO were evaluated for the 80, 137, 260 micron diameter particle sizes (D50) in 2-7 ms-1 velocity by wind tunnel. The results showed that relative efficiency of CDSC is higher than CDS, MWAC, and MDCO as a consequence of the wind speed. CDSC and CDS relative efficiencies varied in relation to wind velocity, but MWAC, MDCO relative efficiencies remained constant. Also, CDSC, CDS, MWAC, MDCO relative efficiencies varied from 0.8, 0.48, 2.18, 0.58 times by increasing the particle size diameters from 80 to 260 micrometers, respectively.
M. Jabarifar, B. Khalili Moghadam, M. Bodaghabadi Bagheri,
Volume 20, Issue 75 (Spring 2016)
Abstract
Splash erosion is one of the most important water erosion types, causing initiation of other types of water erosion. The objective of this study is to model the splash erosion using fuzzy logic approach in part of northern Karoon basin. The major land usage in the area are irrigated farming, dry land farming, pasture and degraded pasture. For the purposes of this study, soil properties including organic matter; CaCO3; surface shear strength (SSS); particle size distribution; mean weight diameter (MWD) and soil splash erosion were measured under four different slope conditions (S:%) and rainfall intensity (RI:mm.h-1): 5-50, 5-80, 15-50, 15-80, respectively, using multiple splash sets (MSS) at 80 different locations. Splash erosion was modeled based on combinational rule of inference under five conditions for selection of different operators. The efficiency of the models was evaluated using mean square error (MSE) between observed and estimated values. Results revealed that all models are capable of predicting splash erosion. Also slope, rainfall intensity, MWD, SSS, fine sand and coarse silt attributes were found to be appropriately and precisely using splash erosion.
A. Khalili Naft Chali, A. Shahidi, A. Khashei Siuki,
Volume 21, Issue 3 (Fall 2017)
Abstract
In recent years and in many countries, overusing groundwater resources had been higher than their annual feeding amount. This issue caused drop in the groundwater levels, followed by drying wells, qanats and springs. In this study, given the importance of Neyshabur plain in supplying agricultural, industrial and drinkable water of the area, lazy algorithms of KNN, KSTAR and LWL and M5 tree model have been utilized under seven different scenarios in order to estimate groundwater level of this aquifer. To compare the results, the Statistical parameters of root mean square error, correlation coefficient and the average absolute error were analyzed. The results showed that the ‘f’ scenario which contains the volume of water discharged and total precipitation parameters is less efficient because the ground surface level parameter was not taken into account. In ‘a’, ‘b’ and ‘g’ scenarios, an optimum estimation has been maintained for the groundwater level by considering the parameters of total rainfall in the previous month, total rainfall in the last two months and the ground surface level. Among the models of lazy algorithms and M5 decision tree model, the ability of KNN model under ‘a’ scenario was more than other models in December (Azar) by the statistical parameters RZ=0/96 , RMSE= 6.56 and MAE= 3.53. Also, study of evaluation criteria showed that the LWL is not an appropriate model to predict the level of the water table.
Z. Sorkheh, B. Khalili Moghaddam,
Volume 22, Issue 1 (Spring 2018)
Abstract
The purpose of this research was to study the effects kerosene by a factorial experiment in the nested design in three replications. The factors included region (Shush, Dezful and Bavi), plant (parsley, dill, coriander and carrot), and management practice (control, contaminated field with kerosene 1, contaminated field with kerosene 2). Heavy metals concentration (Pb, Zn, Cu and Cd) was measured in soil (DTPA extraction method) and plants samples. The results indicated that the average values of the heavy metals concentration in both soil and plants samples subjected to kerosene contaminated treatments were greater than those of the control treatment in all of the regions. The Bavi region had the highest Cd (14.29 in soil; 11.9 in Dill) and Pb (40.46 in soil; 35.53 in Coriander) and the lowest Zn (34.75 in soil ; 28.44 in Carrot) and Cu(22.30 in soil; 16.96 in Carrot) concentration values in both soil and plants subjected to kerosene contaminated treatments. Also, the lowest concentration values of Cd (9.33 in soil; 8.01 in Carrot) and Pb (30.36 in soil; 23.54 in Carrot) and the highest values of Zn (109.08 in soil; 86.33 in Dill) and Cu (47.71 in soil; 38.57 in Dill) were recorded in Shush and Dezful regions, respectively. Based on these findings, kerosene usage could lead to a significant increase in the heavy metals (Cd, Cu and Pb) uptake, exceeding the critical level for the vegetables. This might increase the transformation risk of the mentioned heavy metals in the food chain
H. Faghih, J. Behmanesh, K. Khalili,
Volume 22, Issue 1 (Spring 2018)
Abstract
Precipitation is one of the most important components of water balance in any region and the development of efficient models for estimating its spatiotemporal distribution is of considerable importance. The goal of the present research was to investigate the efficiency of the first order multiple-site auto regressive model in the estimation of spatiotemporal precipitation in Kurdistan, Iran. For this purpose, synoptic stations which had long time data were selected. To determine the model parameters, data covering 21 years r (1992-2012) were employed. These parameters were obtained by computing the lag zero and lag one correlation between the annual precipitation time series of stations. In this method, the region precipitation in a year (t) was estimated based on its precipitation in the previous year (t-1). To evaluate the model, annual precipitation in the studied area was estimated using the developed model for the years 2013 and 2014; then, the obtained data were compared with the observed data. The results showed that the used model had a suitable accuracy in estimating the annual precipitation in the studied area. The percentages of the model in estimating the region's annual precipitation for the years 2013 and 2014 was obtained to be 7.9% and 17.3%, respectively. Also, the correlation coefficient between the estimated and observed data was significant at the significance level of one percent (R=0.978). Furthermore, the model performance was suitable in terms of data generation; so the statistical properties of the generated and historical data were similar and their difference was not significant. Therefore, due to the suitable efficiency of the model in estimating and generating the annual precipitation, its application could be recommended to help the better management of water resources in the studied region.
Miss M. Halil, N. Ghanavati, A. Nazarpour,
Volume 23, Issue 1 (Spring 2019)
Abstract
High concentrations of heavy metals in street dust are considered to be a serious risk to the human health and the environment. In this study, 30 dust samples were collected from the pavements in the main streets of Abadan to determine the level of pollution of heavy metals in the street dust. Heavy metal concentrations were analyzed by inductively coupled spectroscopy (ICP-OES) method. The level of heavy metals pollution was estimated based on enrichment factor, pollution index and Nemro Integrated Pollution Index. The average concentrations of heavy metals such as Pb, Zn, Cu, Cr, Cd, Ni, V, As and Co were 59.13, 287.50, 112.97, 50.03, 0.52, 56.77, 35.83, 7.10 and 7.53 mg/kg, respectively. Based on the average enrichment factor (EF), Ni, Cu and Pb had high levels of contamination and Zn contamination was high. According to the mean of pollution index (PI), heavy metals of Zn and Pb had a high contamination. According to the Nemro Integrated Pollution Index (NIPI), 96.66% of the samples had a high degree of contamination. The spatial distribution pattern of the heavy metals concentration showed that in the areas with high population densities, high traffic volumes and urban shopping centers, heavy metal pollution was severe.
S. Khalilian, M. Sarai Tabrizi, H. Babazadeh, A. Saremi,
Volume 24, Issue 4 (Winter 2020)
Abstract
In the present study, the SWAT hydrological model was developed for the upstream of the Zayandehrood dam to evaluate the inflow to this dam. Accordingly, after entering the meteorological and hydrometric information of the region, the runoff simulation was performed. Due to the high volume of entrances to the Zayandehrood Dam, Shahrokh Castle hydrometric stations were selected as the base station for calibration and validation during the statistical period of 1990-2015. After hydrological simulation and accuracy of results, climate prediction was performed using the fifth model of the climate change for the RCP scenarios. According to the forecast, by using climate change models, the temperature could be assumed to increase in all models and the highest rate of increase would occur under the RCP 8.5 climate scenario. After evaluating climate change in different diffusion scenarios, the runoff of the basin was simulated in the SWAT model. The simulation results of runoff in the catchment area showed that although the amount of rainfall was increased in the region, increasing the temperature had a greater effect, reducing the amount of runoff in the basin. Based on the results of climate change, hydrological simulation was performed using the SWAT model. The results showed that the effect of diffusion scenarios in the region was different, causing an increase in temperature and precipitation. The highest increase was observed in the RCP8.5 scenario, which was consistent with the nature of this emission scenario, with the highest emission of greenhouse gases and carbon dioxide. Then, the evaluation of the hydrological model was done; the results showed that although the amount of rainfall in the region had been increased, the increase in temperature of this basin had a greater effect and efficiency in reducing the amount of runoff.
B. Khalilimoghadam, A. Siadat, A. Yusefi,
Volume 25, Issue 1 (Spring 2021)
Abstract
Dust deposited on the leaves of trees can be effectively used as the monitors of polycyclic aromatic hydrocarbons (PAHs). The dust deposited on the leaves can be used as an appropriate index for evaluating PAHs in the atmosphere. This research was conducted to determine the origin and health risk assessment of PAHs accumulated on the leaves of trees in the city of Ahvaz. For this purpose, samples were taken at leaves on 10 points with different land uses including industrial, recreational, high-traffic and residential ones. After preparation, to determine the type and concentration of PAHs, the compounds were analyzed by GC-MS. The results showed that 15 types of PAHs had been identified from 16 important compounds identified by EPA in the dust samples. The concentration of compounds was the range of 3.3-110 microgram per kilogram. The maximum and minimum of PAHs carcinogenic in particles trapped on leaves were in the Kut-Abdolah with 530 ppb and Shahrvand Park Station with 5.13 ppb, respectively. Also, the average relative of LMW/HMW in the aromatics contained in the deposition of particles on trees was 0.5; further the analysis of the main components of aromatic hydrocarbons (PAHs) showed that there was no specific source for these compounds in Ahvaz, and these compounds could be from fossil fuels, urban traffic, natural gas, generally showing a pyrogenic origin.
S. Abdi Ardestani, B. Khalili, M. M. Majidi,
Volume 25, Issue 1 (Spring 2021)
Abstract
Long-term drought effect is one of the main factors of global climate change, with consequences for soil biogeochemical cycling of carbon and nitrogen and the function of soil ecosystem under drought conditions. We hypothesized that 1) the Bromus inermis, Dactylis glomerata and festuca arundinacea species would differ in their rhizosphere responses to drought and 2) combined plant species and drought would have offsetting effects on the soil biological traits. We tested these hypotheses at the long-term drought field expreiment at the Lavark Farm of Isfahan University of Technology by analyzing soil microbial biomass carbon and nitrogen and activity of β-glucosaminidase in the rhizosphere of Bromus inermis, Dactylis glomerata and festuca arundinacea species. Soil microbial biomass carbon and nitrogen responses to drought depended on plant species, such that the highest MBC was recorded in the Bromus inermis rhizosphere, while the lowest was in the Dactylis glomerata rhizosphere, thereby suggesting the greater microbial sensitivity to drought in the Dactylis glomerata rhizosphere. Genotype variations (drought tolerate and sensitive) mostly affected the change in the β-glucosaminidase activity, but they were not significantly affected by drought treatment and plant species. In general, the positive effects of the plant genotype could offset the negative consequences of drought for soil microbial biomass and traits.
N. A. Zakavi, H. A. Nadian, b. Khalilimoghadam, A. A. Moezzi,
Volume 28, Issue 2 (Summer 2024)
Abstract
Arbuscular mycorrhizal fungi are highly important in improving plant growth and decreasing the negative effects of contaminants. The objective of this study was to evaluate the effect of inoculation of mycorrhizal fungus on the concentration of lead (Pb) and cadmium (Cd) by parsley (Petroselinum sativum) in heavy metal-contaminated soil in the presence of kerosene. This study was carried out as a factorial experiment based on the randomized complete design with four replications under greenhouse conditions. Experimental factors were included: 1- microbial inoculation in two levels with mycorrhizal fungus (Rhizophagus irregularis) and control (without inoculation), and 2- kerosene in four levels of 0, 4, 8, and 12 mL kg-1 soil. The results showed that mycorrhizal inoculation led to a significant increase in root (61.1 to 150.1%) and shoot dry weight (9.1 to 51.5%), shoot P, Zn, and Cu concentration and root Pb (18.7 to 97.9%) and Cd (13.3 to 98.6%) concentration, while significantly decreased shoot Pb (10.0 to 29.2%) and Cd (19.6 to 72.1%) concentration. The root bio-concentration factor (BCF) (7.74%) was higher than compared to shoot BCF. The mycorrhizal inoculation decreased the translocation factor (TF) of Pb and Cd. The TF<1 shows that the mycorrhizal fungus immobilized Pb and Cd in the roots and prevented their translocation from the root to the shoot. Hence, mycorrhizal inoculation can be effective in contaminated soils through bioconcentration of Pb and Cd in the root and decrease their translocation to the parsley shoot.
Sh. Amiri, B. Khalili,
Volume 29, Issue 1 (Spring 2025)
Abstract
Soils are continuously exposed to large amounts of engineered nanoparticles, particularly silver nanoparticles (AgNPs), which can affect soil microbial activities and nitrogen cycling. The hypotheses of the present study were: (i) vegetation types would differ in their responses to Ag types and concentrations, (ii) these responses would be linked to changes in soil protein and amino acid concentrations, and (iii) combined plant root systems alongside Ag types and concentrations would have offsetting effects on soil protein and amino acid concentrations. A greenhouse experiment was conducted to test these hypotheses using a factorial arrangement of treatments within a randomized block design. Two soil types with loamy sand and sandy loam textures were collected from agricultural fields in Isfahan, specifically from the Badroud (33◦ 44′ 50" N, 51◦ 57′ 55" E) and Femi (33◦ 42′ 17" N, 51◦ 59′58" E) regions. The treatments included: 1) soil types (loamy sand and sandy loam), 2) root systems (non-planted, wheat, and safflower), 3) Ag types (no Ag added, AgNPs, and AgNO3), and 4) Ag concentrations (50 and 100 ppm). The plants were harvested 110 days after sowing, with soil samples collected from both the root zone and non-planted soil, after which the concentrations of protein and amino acids were measured. In the Badroud soil, protein concentration significantly decreased (p < 0.05) with increasing depth. Although depth changes did not show a significant difference in protein concentration in the soil under wheat cultivation, increasing depth resulted in a significant decrease (p < 0.05) in protein concentration in the soil under safflower cultivation. In the Fami soil, the addition of silver nitrate led to a significant (p < 0.05) increase in protein concentration, despite the fact that the addition of silver nanoparticles had no significant (p < 0.05) effect on soil protein concentration. In the Badroud soil, the highest concentration of soil amino acids was observed in the silver nitrate treatment, while the silver nanoparticle treatment did not significantly affect soil amino acid concentrations (p < 0.05). However, applying silver treatments at both tested concentrations resulted in a significant increase (p < 0.05) in soil amino acid levels. Overall, the effects of nanoparticles varied depending on the measured parameters (protein or amino acid), soil texture, and type of cultivation. Further studies are needed to determine the mechanisms by which AgNPs and AgNO3 affect the soil nitrogen cycle in the presence of plants at different soil depths.
Ali Akbarian Khalilabad, Hojat Karami, Seyed Farhad Mousavi,
Volume 29, Issue 3 (Fall 2025)
Abstract
The reduction of soil permeability due to the sedimentation of suspended particles is a significant challenge to the efficient operation of artificial recharge systems. In this study, the effects of sediment concentration (0.5, 2, and 4 g/L), soil particle size, and vertical distribution on clogging processes were investigated using laboratory soil column experiments. The results showed a two-phase decrease in permeability: a rapid initial drop caused by the blockage of coarse pores during the first 10 minutes, followed by a second phase where the system reached a relative equilibrium. Higher sediment concentrations led to a faster decline and lower equilibrium values of permeability. Fine-grained soils, despite having lower initial permeability, demonstrated greater resistance to clogging, while coarse-grained soils experienced more severe reductions. Vertical analysis indicated that the most significant permeability loss occurred at a depth of 40-50 cm, while deeper layers showed increased permeability due to the limited penetration of suspended particles. These findings can inform the selection of appropriate materials, the design of subsurface layers in recharge basins, the prediction of system lifespan, and the regulation of sediment load in inflows to enhance the efficiency and sustainability of artificial recharge systems.
Homa Chegini, Chooghi Bairam Komaki, Majid Owneq, Hamidreza Asgari, Khalil Ghorbani,
Volume 30, Issue 1 (Spring 2026)
Abstract
This study aimed to analyze the spatial–temporal correlation between the Vegetation Health Index (VHI) and climatic variables, including precipitation, potential evapotranspiration (PET), and mean temperature, in Golestan Province during the period 2000–2024. MODIS satellite products were used for vegetation and land surface temperature data, while the TerraClimate dataset provided precipitation and PET variables. After spatial–temporal alignment, the Cross-Correlation Function (CCF) was applied to identify optimal time lags, and the Random Forest model was employed to assess the relative importance of the climatic drivers. Turning to the results, increasing trends in mean temperature and PET were observed, alongside a significant decrease in precipitation, which led to intensified climatic stress and reduced VHI across the province, especially during summer in croplands and rangelands. The relationship between VHI and precipitation was positive (maximum correlation of 0.299 in croplands), negative with PET (−0.287), and non-linear with temperature (0.275). Notably, VHI responded to precipitation with short-term lags (0–1 month), whereas PET and temperature effects emerged with longer lags (2–4 months). The Random Forest analysis highlighted precipitation as the most influential factor on VHI, followed by PET and temperature, achieving strong predictive performance (R² = 0.78, RMSE = 0.09). Overall, these findings emphasize precipitation as the immediate driver of vegetation health, while PET and temperature act as secondary, cumulative stressors. The results provide valuable insights for developing climate adaptation and sustainable resource management strategies in agriculture and natural ecosystems of Golestan Province.
