Iranian Journal of Soil and Water Research
Year:2020 | Volume:51 | Issue:4|Papers Count:20

Long-term planning and proper management of groundwater resources utilization are essential to ensure a reliable supply of water to countries, especially in arid and semi-arid regions. Therefore, it is necessary to employ appropriate models to predict the spatial and temporal fluctuations of aquifers and their future behavior. This study aimed to apply zoning strategies to Miandoab aquifer and predict its spatial and temporal groundwater level using an artificial neural network. First, the six parameters of transmissivity coefficient, groundwater level, ground elevation, withdrawal, rainfall, and discharge were spatially clustered to identify their effect on the simulation model. Three clustering approaches of single-parameter, three-parameter and integrated-parameter were evaluated using some statistical indices. The number of suitable clusters was determined using silhouette width. Groundwater level data (2002-2012) from 77 observational wells were used for model training and validation. Results showed that the correlation clustering approach performs better than the other methods. Precipitation, aquifer recharge, aquifer discharge, and groundwater level of the previous month were inputs to the back-propagation artificial neural network (ANN) for predicting a two-year period of groundwater level. The results showed that the correlation coefficients of variation in 6 clusters were 0. 71-0. 97, and the RMSE variations were 0. 19-0. 58, indicating appropriate accuracy of this approach for predicting groundwater level.

During the plant growing season, soil salinity changes by irrigation water. Therefore, assuming constant soil salinity during the growing season cannot simulate the actual plant growth conditions in the field. Accordingly, the present study aimed to investigate the interaction of irrigation water salinity (at six levels including 1. 2, 4, 6, 8, 10 and 12 dS. m-1 ) and salinity of soil saturated extract at the beginning of the season (at three levels including 3, 4. 5 and 6 dS. m-1 ) on growth, yield components, quantitative and qualitative yields and water use efficiency of a cultivar of canola, Hyola 401. For this purpose, a factorial experiment based on randomized complete block design with three replications was conducted in the Gorgan region. The results showed that the effect of initial soil salinity and irrigation water salinity on final soil salinity was significant. However, according to correlation analysis and regression equation, the dependence of final soil salinity on irrigation water salinity was higher than the initial soil salinity. The salinity of water and soil had no significant effect on the number of secondary branch and harvest index but they resulted in the significant increase of days to flowering and significant decrease in days to maturity, plant height and height of the first silique, stem diameter, silique length, number of seeds per silique, number of silique per plant, and biological yield. Due to the high tolerance of canola to the soil salinity, seed yield and seed water use efficiency (on the basis of seed yield and economy) were not affected by soil salinity, but the salinity of irrigation water caused a significant decrease of these traits because of the introduction of frequent salinity shocks into the plant. Also, the interaction of irrigation water salinity and soil salinity on oil percent, oil yield, and oil water use efficiency was significant so that an increase in irrigation water salinity from 1. 2 to 12 dS. m-1 at different soil salinities of 3, 4. 5 and 6 dS. m-1 resulted a decrease of oil percentage by 10. 1%, 7. 3%, and 10. 5% and oil yield and oil water use efficiency by 23%, 18% and 28%, respectively.

Phosphorus is one of the macronutrient that its deficiency severely restricts plant growth. One of the simplest and least costly methods of providing phosphorus is direct application of rock phosphate but in calcareous soils it is not very effective due to its low solubility. The use of rock phosphate mixed with sulfur and organic matter along with phosphate solubilizing microorganisms is considered as a method for increasing rock phosphorus solubility. This study aimed to model the effect of different ratios of vermicompost, rock phosphate and sulfur on dissolution and release of phosphorus by Aspergillus sp and to optimize the levels of these variables for efficient biofertilizer preparation. Accordingly, 20 experiments were designed using response surface methodology based on central composite design. The effects of different values of vermicompost, rock phosphate and sulfur variables encoded in the constraint (+1, 0,-1) on the dissolution rate of phosphorus were modeled. The results showed a high efficiency (R 2 = 0. 8841) of the central composite design model in estimating P dissolution. The results also indicated that vermicompost interaction with sulfur (p <0. 05) and interaction of rock phosphate with sulfur (p <0. 05) were significant. The results of the statistical analysis of the central composite model coefficients indicated that the vermicompost, vermicompost*sulfur and rock phosphate*sulfur additives had a positive and incremental effect on the phosphorus solubility. According to prediction of optimum conditions for phosphorus solubilization, 58% vermicompost, 23. 3% rock phosphate and 18. 7% sulfur resulted in maximum phosphorus solubilization (773. 04 mg / kg) by Aspergillus sp. in microbial fertilizer.

Investigation on available freshwater below the sea in offshore aquifers (i. e. part of the coastal aquifer is stretched beneath the sea) can provide a resource for ecological demand in the future. In this study, to understand the response of intruded seawater to freshwater recharge that is leaked from the riverbed, various scenarios with different aquitard length and permeability or river conductance have been introduced. SEAWAT dispersive code is used for seawater intrusion simulation and saltwater wedge toe position, mixing zone thickness and saltwater volume were estimated. The results showed that the river conductance influences the volume of freshwater in offshore aquifers significantly. Thereby, small conductance value (i. e. 0. 0025 m 2 /d in this study) leads to a negligible impact on saltwater characteristics but as this coefficient grows up to a specific amount, more freshwater is released from the riverbed which could significantly affect saltwater characteristics (60% reduction in brackish water volume). Nonetheless, extra-large conductance (i. e. 375 m 2 /d in this study) cannot sharply affect seawater recession due to high-velocity magnitude of recharged freshwater and limited contact time with the saltwater wedge. Hydraulic conductivity and length of aquitard also affect the results; so that the extension of aquitard length to the dry land boundary will cause reduction in the recharged freshwater and consequently reduce the intruded saltwater especially in the vicinity of aquifer bed.

The reliability and validity of extreme floods, especially the probable maximum flood (PMF), requires to consider uncertainty sources in flood estimation. Parameters uncertainty of rainfall-runoff models are the main sources of uncertainty in flood estimation. In this paper, the Monte Carlo method has been used to estimate the PMF hydrograph uncertainty due to uncertainty in the calibration parameters of the rainfall-runoff model in Bakhtiary Basin in southwestern of Iran. The HEC-HMS hydrologic model was used to estimate the PMF hydrograph resulted by the probable maximum precipitation (PMP). The SCS curve number, Clark's unit hydrograph and Muskingum methods were used to model losses, rainfall-runoff transform and river flood routing, respectively. The results show that the uncertainty in peak discharge and volume of PMF hydrograph due to the uncertainty of all parameters are 17. 13 and 6. 79%, respectively. The results showed that the uncertainty in peak discharge and PMF hydrograph volume due to uncertainty of all parameters are 17. 13 and 6. 79 percent respectively. The uncertainty in peak discharge of PMF hydrograph due to curve number, initial losses, concentration time, Clark's storage coefficient, Muskingum K and Muskingum X parameters are 5. 05, 0. 4, 3. 78, 3. 85, 4. 05 and 0. 01 percent respectively. Also, the uncertainty in the PMF hydrograph volume due to the uncertainty of the curve number, initial losses, concentration time, Clark's storage coefficient, Muskingum K and Muskingum X parameters were 4. 46, 0. 332, 0. 328, 1. 6, 0. 08 and 0. 0002 percent respectively. Therefore, in order to reduce the uncertainty in estimating PMF hydrograph, it is necessary to be more precise in estimating the parameters of curve number, Muskingum K, Clark's storage coefficient and concentration time, respectively.

Olive has been well adapted to arid and semi-arid climates due to leaf structure and drought tolerance mechanisms. Zard is a native double-purpose cultivar and the most important olive cultivar in Iran. This study was conducted to investigate the effect of drought stress on some metabolic, morphological and physiological traits of Zard cultivar plants under greenhouse conditions. Oneyear old plants (self-rooted) of Zard cultivar were used in this study. Self-rooted plants were transferred to 10 kg pots containing 1: 1: 1 sand, field soil and cocopeat and kept for six months with optimal irrigation and nutrition conditions. By considering the field capacity as control treatment, three treatments of 75, 50 and 25% of field capacity were defined as drought stress (totally four treatments) and the experiment was continued for 60 days. The experimental design was a completely randomized design with four drought stress treatments in three replications and two plants per replicate. Results showed that the moderate and severe drought stress significantly reduced stomatal conductance and transpiration and consequently reduced the differences between the leaf and air temperatures. Despite of decrease in photosynthesis rate, its difference was significant in stresses greater than 0. 5FC, indicating that the drought tolerance mechanisms were activated in this cultivar. Reduced photosynthesis rate was due to reduction in chlorophyll content per unit leaf area and also in carbon dioxide assimilation and consequently reduction in stomatal conductance. Increased accumulation of proline and total phenol caused to boost the plant tolerance to drought stress. Based on the responses of olive plants to drought stress, it was concluded that Zard cultivar was tolerant to drought stress up to 0. 5FC level. According to growth indices, Zard olive cultivar can be recommended for orchards in regions with scarcity or marginal water conditions.

Estimating infiltration amount is very important in irrigation canals. In this regard, there are different methods to obtain the infiltration amount. The studies done to estimate the seepage rates in Iran’ s irrigation networks are based on the mass balance method. Ponding test is a benchmark method to determine the canal seepage rate. In this method, a specific distance of the canal is isolated and the seepage rate is obtained by measuring the water level reduction in the pond. In this study, the feasibility of using conventional infiltration models; Kostiakov-Luis, Philip, Green-Ampt, and S. C. S has been investigated to estimate the seepage rate based on the ponding test data. Employing the field data of some ponding tests carried out in irrigation network of Australia, it was found that Kostiakov-Luis, with mean absolute relative errors between 2. 3% to 7. 4%, is the best model to estimate the infiltration rate in the canal. Although the current methods give a constant seepage rate irrespective of the canal flow depth, the outcome of this study indicates that the seepage rate is not a constant value. It depends significantly on the elapsed time, water level, and soil moisture. Also, it was found that the proposed method is less sensitive to the data gathering duration compared to the current methods.

Soil temperature (Ts) is a key factor controlling the soil physical, chemical and biological properties and processes and consequently affects agricultural crop productions. The objective of this study was to estimate Ts from meteorological data using different machine learning methods. For this purpose, meteorological data and soil temperature at different depths (5, 10, 20, 30, 50 and 100 cm from the soil surface) for 25 years (19942018) were collected from 17 synoptic stations in Khuzestan province, Iran. Air temperature, wind speed, relative humidity, evaporation, precipitation, and vapor pressure were used as inputs to train the models. Multiple Linear Regression (MLR), Multilayer Perceptron Neural Network (MLPNN) and Extreme Learning Machine (ELM) models were used to predict soil temperature from metrological data. The results indicated that all models predicted temperature of the top layer (0-30 cm) better than the ones in sublayers. on the other hand, by increasing soil depth the accuracy of the models diminished; so that, the best and worst Ts predictions were belong to 5 cm and 100 cm depth, respectively. The results revealed that MLR, MLPNN and ELM models provided desirable performance in modeling Ts at all depths, with R 2 values ranging 0. 700-0. 864, 0. 967-0. 997, and 0. 967-99, RMSE values ranging 2. 557– 2. 873, 0. 034– 0. 072, and 0. 028– 0. 078 ° C, and MAE values ranging 1. 398– 1. 529, 0. 023– 0. 063, and 0. 023– 0. 065 ° C, respectively. Overall, the results showed that MLPNN and ELM models had approximately similar performance and better accuracy than MLR model. However, because of the high computational speed of the ELM model, it is recommended to use MLPNN model for estimation of soil profile Ts.

Adsorption of heavy metals by metal oxides is an effective method that is using to reduce the effects of heavy metals in recent years. The aims of this research were to compare the effects of non-stabilized and Nacarboxymethylcellulose (Na-CMC)-stabilized hematite on the immobilization of nickel and to investigate the effects of these amendments on different chemical forms of this metal in soil. For this purpose, a factorial experiment was conducted using a completely randomized design. The experimental factors were types and dosages of adsorbents (two types of adsorbents including non-stabilized and Na-CMC-stabilized hematite in four levels, including 0, 0. 25, 0. 5 and 1%) and the levels of soil total Ni (25, 75, 125, 175 and 325 mg kg-1). The results showed that the application of adsorbents to the soil decreased the concentration of Ni extracted by DTPA (Ni-DTPA) and MgCl2 (Ni-MgCl2). The concentrations of Ni-DTPA and Ni-MgCl2 decreased as the amount of adsorbent amount increased from 0. 25 to 0. 5%, but they increased as the amount of adsorbent increased from 0. 5 to 1%. The reduction concentration rate of Ni-DTPA by application of 0. 25, 0. 5 and 1% non-stabilized hematite were 11, 13. 9 and 9. 63%, and by Na-CMC-stabilized hematite were 23. 7, 35. 9 and 20. 3%, respectively, as compared to the control treatment. The results also showed that there were significant differences between Ni-DTPA and Ni-MgCl2 concentration in the non-stabilized and Na-CMC-stabilized hematite treatments. The results of sequential extraction in treatment with 175 mg Ni kg-1 soil showed that the adsorbent application significantly reduced soluble + exchangeable, carbonate forms of Ni, and increased the form associated with iron and manganese oxides compared to the control treatment.

Wind erosion is one of the most important environmental issues in the world. This phenomenon could have hazardous effect on weather, water quality and human health. In recent years, the use of mulch has been considered for controlling wind erosion and soil stabilization. The aim of this study was to use Montmorillonite Nano clay, Polyvinyl Acetate polymer and wheat straw biochar as a natural mulch for controlling wind erosion and soil stabilization. A factorial experiment was conducted in a completely randomized design with three replications. The factors included (1) mulch (three types), (2) mulch concentration (Nanoclay: 0, 16 and 32, Polyvinyl Acetate polymer: 0, 8, and 16 and biochar: 0, 65 and 200 g/m2) and (3) time duration (21, 42 and 63 days). Two soil samples (sandy and loamy sand) were collected from the dust source of southeast of Ahvaz (center Number. 4). Trays with a 500×300×50 mm were filled by the soils. Then, the emulsion of Nanoclay, and Polyvinyl Acetate were sprayed uniformly on the soil surface. The biochar also was uniformly mixed with the soils. The trays were placed on wind tunnel channel and wind erosion test was performed at a speed of 20 m/s for 5 minutes. Then, aggregate stability (MWD), penetration resistance (PR), shear strength (SS) and their changes were measured at all duration times. The amount of soil loss at first duration time in nanoclay, polymer and biochar treatments were decreased in sandy soil 98%, 97% and 43. 65%, and in loamy sand soil 97%, 95% and 58%, respectively as compared to the control treatment. Maximum MWD, PR and SS was obtained in the nanoclay treatment. In general, the results showed that the use of nanoclay, polymer and biochar materials significantly reduced wind erosion at different duration times, compared to the control treatment.

Despite the development of technical tools for the analysis of complex systems, the most important issue in resolving water resource problems focuses on the interaction of human and natural systems. Agent based modeling (ABM) has been used in recent years as an effective tool for the development of integrated human and environmental models. One of the main challenges of ABM application in water resources management is to identify and characterize key agents. In this study, an ABM has been developed for optimal water allocation from dam reservoir to downstream needs. The proposed model includes reservoir simulation using Standard Operating Policy, optimization of water allocation using Genetic Algorithm, and behavioral simulation of agents using ABM. The behavioral simulation model simulated stakeholders interactions and their reactions to water allocation decisions. To develop this model, key agents including drinking, industry, agriculture, environment and policy sector were identified in the Tajan basin. The proposed model is a new method for simulation of stakeholder interactions and establishing a hydrological-environmental-human relationship to manage demand and to optimize water allocation to water needs. The obtained results showed that the proposed model has a high potential for increasing agent utility and total profit through agents feedback analysis, so that agricultural agent utility increased from 19% to 47% and the total profit increased up to 21. 4%.

In order to study and evaluate drought stress indices in surface irrigation on grain yield and yield components, oil yield, seed oil content and grain water use efficiency, an experiment was conducted at Behbahan Agricultural Research Station in 2013-2014. The experiment was conducted as split plot in a randomized complete block design with 3 replications. Irrigation at two levels (irrigation after 100 and 200 mm evaporation from Class A pan, respectively) was evaluated as main factor and sesame cultivar at 9 levels as sub-factor. Comparison of mean water use efficiency in irrigation and cultivar interactions showed that 100 mm evaporation from Class A pan and V9 cultivar (SG55-92138) with water use efficiency of 0. 272 kg/m3 sesame seed was superior. The mean consumed water in one year of experiment was 547. 5 and 438. 6 mm, respectively in 100 and 200 mm evaporation from Class A pan. Pearson correlation coefficient results showed that with increasing number of capsules per plant, number of seeds per capsule, 1000-grain weight, water use efficiency, grain oil yield and oil water use efficiency increased. Due to higher STI, MP, GMP, HM and YI indices in V9 cultivar compared to other sesame cultivars, V9 sesame was introduced as superior treatment for drought stress. Decreasing trend of sesame yield which was caused by water deficit stress, decreased SSI, MP and TOL indices. Inversely, reduction of sesame yield led to incremental changes in STI, GMP, HM, YI and YSI indices.

The study of infiltrated solutes and their pathways toward the rivers is very important in water quality studies of rivers and groundwaters well as the management of fertilizers and pesticides in the farms. In this research, the transport of infiltrated solutes toward the connected groundwater and surface water was experimentally studied. The concentration of solutes and the discharges were measured separately in the groundwater and surface water, using a creative modification in the current experimental model of 150 cm length, 70 cm high and 20 cm width. Potassium permanganate was used to observe the pollutant cloud movement and sodium chloride was used to measure the amount of transported solute. Potassium permanganate was injected in places of 15, 35, and 55 cm far from the river while NaCl was injected in places of 15, 35, and 65 cm far from the river. In all experiments, the hydraulic head difference was 33 mm in the model. The tracer study showed that the capillary zone affects solute transport and the tracer starts to move horizontally above the groundwater table. When the NaCl injection starts, due to changes in the boundary condition, the river discharge decreases while the groundwater discharge increases. Continuous measurement of EC in groundwater and surface water showed that the river is affected more than the groundwater by receiving 60% of the injected salt.

In present study, the groundwater level of the Kabodarahang region located in Hamadan Province was simulated using novel techniques such as Self-Adaptive Extreme Learning Machine (SAELM) and WaveletSelf-Adaptive Extreme Learning Machine (WA-SAELM). Firstly, the effective lags were detected using the autocorrelation function and then ten models were developed for each SAELM and WA-SAELM methods. By evaluating the results of the models, WA-SAELM was introduced as the superior method. The analysis of the simulation results showed that the superior model had a high accuracy in estimating the groundwater level. For the superior model, the correlation coefficient (R), Root Mean Squared Error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSC) were calculated to be 0. 969, 0. 358 and 0. 939, respectively.

Todays water table management (WTM) plays an important role in saving water and nutrients such as nitrate and phosphorous and also in improving downstream water quality. However, the use of WTM in hot and dry areas such as Khuzestan may be restricted due to high evapotranspiration and capillary flow. In this study, using plastic lysimeters, the feasibility of using water table control methods (controlled drainage and subsurface irrigation) accom`plished with leaching management was investigated in Dezful climate under Tomato cultivar of Kingstone cultivar. For this purpose, three treatments including free drainage, controlled drainage and subirrigation, each in three replications were considered in lysimeters with 90 cm in heghit and 40 cm in diameter. In this study, the shallow groundwater with salinity of 2. 3 dS/m was kept at a depth of 50 cm from the soil surface in the controlled drainage and subirrigation treatments. The results showed that the percentage of salts mass in drainage water of free drainage and controlled dariange treatments were 65 and 45%, respectively. The percentage of NO3-N and phosphate mass loss in free drainage treatment were 20. 9 and 8. 1%, respectively that were higher than those in controlled drainage treatment with values of 13. 7% and 5. 7%. Since, the electrical conductivity in the root zone in subirrigation treatment did not reach to 3 dS/m, additional leaching did not performed in this treatment. Therefore, the results of this study promise the effectiveness of WTM at 50 cm from the soil surface at laboratory scale and in warm and semi-arid areas.

It is important to have a fast and accurate model for predicting incoming hydrographs in order to reduce the financial and life damage in flood forecasting systems as well as water allocation issues, especially high priority consumption. In this study, using diffusion wave model, inverse flood routing in Yuan River in China was performed using mixing cell and Crank Nicolson numerical method. These numerical methods were chosen because of their high computational speed and no need for river topographic and hydraulic information. For this purpose, Yuan River flood information data for five years were used, then the numerical simulation results were compared with observational data. Accordingly, the statistical indices of coefficient of determination, Nash-Sutcliffe index, Wilmot index and residual sum coefficient were determined. The results showed that both numerical methods have good accuracy. The maximum error for the observed and calculated maximum discharge in Crank-Nicolson and mixing cell methods were 0. 22 and 0. 59%, respectively. Based on the results obtained in the prediction of maximum discharge and inlet hydrograph, it was found that the Crank Nicolson method is more accurate than the mixing cell method.

Zinc (Zn) next to N and P is the most important nutrient that its deficiency is considered to be a serious widespread nutritional disorder of the world’ s rice paddy fields which causes yield reduction. The current field study was conducted to explain the effect of Zn application (basal and foliar) on morphological characteristics, rice yield and yield components, and more broadly, grains bio-fortification (Zn and protein content). The two factors factorial experiment was conducted in a completely randomized design with three replications in the farmer field in 2018-2019. The applied treatments were basal application of zinc sulfate at three levels (0, 10 and 20 kg. ha-1 Zn), and foliar application 0. 5% zinc sulfate (ZFA) in four levels (no ZFA, ZFA at maximum tillering, ZFA at flowering and ZFA at grain filling stages). Basal Zn sulfate was applied before ploughing and thoroughly mixed with the surface plough layer. Results showed that almost all of the collected data except amylose content and gelatinization temperature were significantly affected by basal and foliar application of Zn, and their interactions (P≤ 0. 01 or P≤ 0. 05). The highest value of spikelet per panicle, grains per panicle, 1000 grains weight, grain yield and biomass were found at 20 kgha-1 basal Zn application and foliar application at maximum tillering and flowering stage, meanwhile the highest grain Zn content and white rice protein content were observed at 20 kgha-1 and foliar application at grain filling stage. The highest Zn content in all rice tissues were obtained at 20 kg. ha-1 basal application. The maximum Zn content in leaves and stems, roots, and panicles were found by foliar application at maximum tillering, flowering and ripening stages, respectively. It can be concluded that in order to produce higher grain and straw yield, the higher Zn treatment and earlier foliar application, whereas for quality factors, latter stage application were effective.

Climate change is one of the most important factors influencing the hydrological cycle. The present study investigate the impact of climate change on runoff and sedimentation of the Dehbar watershed for the next 30 years using statistical model, LARS-WG and conceptual hydrological model, SWAT and WEPP. First, LARSWG model was calibrated to generate future meteorological data in the basin. Then, the HadGEM2 model with three scenarios; RCP8. 5, RCP4. 5 and RCP2. 6 were used to downscale meteorological data for period of 2020 to 2050. The results indicate a decrease in rainfall and an increase in temperature on the horizon of 2050. Also, the results of SWAT model show the runoff variations for RCP8. 5, RCP4. 5 and RCP2. 6 scenarios which are 22. 16%, -17. 93% and-17. 34%, respectively, and for sedimentation variations which are-4. 4%, +9. 4% and +3. 2%, respectively, relative to the base period. In the WEPP model by choosing the hillslop method and using downscaled results of RCP8. 5, RCP4. 5 and RCP2. 6 scenarios, sediment variations were estimated-6. 4%, +12. 6%, and +4. 8%, respectively, compared to the base period.

Soil organic carbon is an important factor in determining the global carbon cycle and global climate regulation. Soil is also the input/output source of carbon to the atmosphere which is depended on the land use. For this purpose, the objective of this study was to compare different methods of data mining in predicting soil organic carbon storage in irrigated, mixed cultivation (irrigated and rainfed), pasture and palm trees lands in some parts of Behbahan city in southwestern of Iran. Soil sampling from depths of 0-30 and 30-60 cm was carried out using conditional Latin hypercube square method. Organic carbon content of the soil samples was determined by Walky-Black method. Bulk density of the soils was determined using paraffin method. The auxiliary parameters used in this study included territory components, OLI sensor image data from landsat 8 and land use map. The results showed that the SAVI, NDVI, NDSI, salinity, carbonate, gypsum and clay indices have the highest correlation with the soil organic carbon stock values. The results also showed that the random forest (RF) (R2= 0. 983, RMSE=2. 32) was the best model to predict soil organic carbon storage followed by artificial neural network model (R 2 = 0. 887, RMSE= 4. 257) and Support Vector Regression Machine model (SVR) (R2 = 0. 707, RMSE=7. 344).

In this study, the effects of land use change (conversion of natural forest to paddy fields) and regeneration of deforested land on some qualitative and enzymatic properties of the soil were investigated. For this study, Populus Research Station in Guilan province, Astana Ashrafieh city was selected. Soil samples were collected from 5 land uses (natural forest, populus, Alnus and Taxodium and paddy forests) and two depths (0-20 and 20-40 cm). The data were analyzed as a factorial experiment in a completely randomized design with three replications. The factors included land use type at five levels and soil depth at two levels that were studied in three replications. Therefore, the number of treatments were 5 * 2 = 10, totally 30 units accounting replications. In total, there were 30 samples of disturbed soil and 30 samples of undisturbed soil that constituted the statistical data of the experiment. Some physical, chemical and biological properties of the samples were measured. The results showed that the mean weight diameter (75%), saturated hydraulic conductivity (95%), water repellency (53%), organic carbon (52%), total nitrogen (52%) and microbial biomass carbon (53%) in the natural forest were higher than the ones in the paddy field. Also, the activity of urease and arylsulfatase in the paddy soil was significantly lower than the one in the natural forest soil. However, the activity of dehydrogenase in the paddy soil was higher than the one in natural forest soil, indicating oxygen availability and moisture affect the activity of dehydrogenase and its activity increases as the soil moisture increases. Reclamation of some deforested lands by hand planted forest showed that Populus vegetation was more effective than the other vegetation covers in increasing aggregate stability, hydraulic conductivity, and microbial biomass carbon. While Taxodium cover increased the activities of urease, arylsulfatase and acid phosphatase more than the ones in other vegetation covers. The specific activity of enzymes (enzyme activity per unit of organic carbon), in dehydrogenase was significantly higher in the paddy soil than in the soil under natural forest. This indicates that the activity of this enzyme is independent of soil organic carbon changes and is dependent on soil moisture. Overall, it can be concluded that, in comparison with the qualitative and enzymatic activity, Taxodium coating improved these properties better than populus and Alnus coating.