مجله تحقیقات آب و خاک ایران
سال پنجاه و سوم شماره 10 (پیاپی 82، دی 1401)

Soil moisture is one of the key parameters in water resources studies and irrigation remote planning. Measuring soil moisture on a large scale is costly and very time consuming. Traditional methods of measuring soil moisture at farm level cannot show the spatial changes of moisture in the best way. Various new methods have been developed to use satellite data to model soil moisture based on thermal images. This study was conducted in 2020 with the aim of investigating the ability of thermal satellite imagery to estimate soil moisture and to plan irrigation rounds of lands in sugarcane industry of Amirkabir located in the south of Khuzestan province. For this purpose, during growing season of sugarcane, soil moisture content was calculated for 9 crossings Landsat 8 satellite and evaluated using 180 ground control points, and also daily irrigation data of 32 farms (25-hectare) were recorded during the study period. The results showed that the accuracy of the model is suitable for estimating soil moisture with the measured values at the farm level. The mean square root of normalized error (NRMSE) was 12.9% and the coefficient of determination (R²) was 0.82. Also, the results of soil moisture in irrigation management of sugarcane fields showed that thermal trapezoidal model is effective due to using thermal bands to environmental factors such as relative humidity percentage, average air temperature, pest (leaf dryness) and plant temperature, and somewhat in June and July causes errors in irrigation planning of sugarcane fields. The mean square root of normalized error (NRMSE) during soil water stress was 24.32%, during irrigation time was 22.20%, at average humidity was 11.7%, during high humidity was 13.20% and during irrigation was 8.86%. Consequently, the accuracy of thermal trapezoidal model for planning irrigation of farms in estimating soil water stress and field irrigation time in some periods of growing season is moderate and for fields having sufficient soil moisture is well.

Bakhtegan and Tashk internationally important Wetland is one of the largest wetland sites in Iran that has dried up in recent years. This study was conducted to estimate the ecological water requirements of this wetland site. For this purpose, first the basic data including long term trend of rainfall, SPI and flow rates of Kor and Sivand rivers as the main feeding sources of wetlands were analysed. Then, to estimate the ecological water requirement, changes in the wetland area in the long run were investigated. Also, changes in Flamingo species as an ecologic index were studied in line with wetlands surface changes. The results showed that the amount of ecological water required to maintain the ecological functions of Bakhtegan and Tashk wetland from the Kor-Sivand River at the entrance of the wetland in dry, normal and wet yearsare 700, 826 and 1037 million cubic meters, respectively. The best times to release the water from upstream sources are February, March and April, respectively. The most important factors contributing in the drought of wetlands include   increase of agrilands to fed excessive increase of the basin population, non-sustainable management of water resources, construction of new dams in watershed, significant reduction of rainfall and increasing of dry seasons during last two decades.

Recently, growth-promoting bacteria have been proposed as complementary and increased efficiency of chemical fertilizers in order to increase soil fertility in crop production in sustainable agriculture. In order to study the effect and efficacy of plant growth promoting bacteria and different rates of phosphorous fertilizer on growth parameters, nutrient availability and wheat yield (Mahdavi variety), a field experiment was conducted as factorial based on completely randomized block design in three replications. Experimental factors include five rates of phosphorous (0, 25, 50, 75, and 100 kg/ha P) and soil inoculation with bacteria at four levels including no-inoculation (Control, I0), inoculation with Azotobacter (I1), inoculation with Pseudomonas (I2) and inoculation with Azotobacter and Pseudomonas (I3). Results of variance analysis showed a significant difference on the effect of seed inoculation and phosphorous levels on plant height, root length and germination percent of wheat seeds. In this research, the treatment of 100 percent P-requirement in the soil inoculated with Azotobacter and Pseudomonas showed a greatest effect on increasing soil phosphorous (27%), growth (28%), yield (27%), yield components (31% grain yiehd) and grain phosphorous (27%) of autumn wheat. Increase in plant dry weight by rhizobacteria is due to increase in nutrients uptake and subsequently better plant growth which could result in higher harvesting index.

This study aims to evaluate and to compare the efficiency of support vector machine (SVM) and random forest (RF) models using digital soil mapping approach to predict soil texture in part of Khuzestan province. In February 2021, before determining soil texture, 200 soil samples were taken using stratified random sampling from the surface layer )0-10 cm(. Auxiliary variables included primary and secondary derivatives of digital elevation model (DEM), remote sensing spectral indices (RS), from which the appropriate category was selected using principal component analysis (PCA). Based on PCA method, nine topographic variables from DEM and eight vegetation indices and spectra from RS were selected to predict soils texture components (sand, silt, and clay). The efficiency of the models was evaluated using the coefficient of determination (R2) and the root mean squared of the error (RMSE). The results indicated that the random forest model had higher accuracy and less error than the support vector machine model (SVM), so that values of R2 in this model were 0.80 for sand, 0.81 for silt, and 0.78 for clay, and the RMSE in the prediction of these particles were 6.02, 5.89 and 6.02, respectively. While the R2 and RMSE in the support vector machine model for prediction of sand, silt and clay were (0.39, 13.70), (0.45, 10.70), and (0.46, 9.32), respectively. Also, the results of this evaluation showed that salinity index, brightness index, and channel network in addition of the 6-band Landsat 8 satellite or the far infrared band were the most important environmental variables predicting clay, silt, and sand particles. In conclusion, we suggest using Random Forest model as a useful and reliable method in preparing digital maps of soil texture in the study area.

Wheat as the main food in the country is of particular importance. Wheat is not only an important economic agricultural commodity in the world, but also known as a powerful lever in political and global relations. Therefore, the analysis and forecast of the production status of this product in the country has always been the focus of attention. The purpose of this study is to predict the amount of wheat yield (X) using artificial intelligence in the annual time scale in Alborz province. For this purpose, using annual cultivation and production data, wheat yield was investigated in six cities of Nazarabad, Savojbalagh, Karaj, Eshtehard, Fardis and Taleghan with a period of 40 years (1981-2020). After calculating the yield (ton per hectare) and forming an annual time series, four artificial intelligence methods including the best neighbor algorithm (KNN), backup vector (SVM), gene expression planning (GEP) and Bayesin Network (BN) were used and the wheat yield was predicted for the following year. Results indicated a more precision in yield prediction in the years with more production; According to the results of the BN, GEP, SVM and KNN model, the correlation coefficient between the observed and anticipated wheat yield values was 0.84, 0.89, 0.89 and 0.92, respectively. Explaining that Karaj and Taleghan cities have the highest and lowest wheat production respectively. The results showed that the KNN method had the best accuracy among the others, as the values of R, RMSE and MAE varied from 0.84 to 0.92, 0.21 to 0/24 and 0.11 to 0.18. Overall, by comparing the proposed methods, the KNN method had the highest and the BN method had the least accuracy to predict the amount of wheat yield in Alborz province. The results of this study can be very useful in providing and managing food security in areas under study.

Crop modeling is a cheap, fast and powerful method to achieve the results of the effect of various factors on crop growth. Hence, crop models such as CERES-Maize have been developed to simulate plant performance. Given that the amounts of irrigation water and nitrogen fertilizer are two very important factors to improve corn yield; it is important to know the accuracy and error of the CERES-Maize model to simulate the yield of this crop under the mentioned treatments. Therefore, the present study was conducted at a 500-hectare farm of the Seed and Plant Breeding Research Institute located at 50.58° East longitude and 35.56° latitude on two corn cultivars (double cross 370 and single cross 260). For double cross 370, two factors including the amount of irrigation water at four levels (W1: 120, W2: 100, WI3: 80 and W4: 60 percent of water requirement) and nitrogen fertilizer at four levels (N1: 100, N2: 80, N3: 60 and N4: zero percent of nitrogen requirement) were considered. For single cross 260, four fertilizer levels (N1: 100, N2: 80 and N3: 60 and N4: 50 percent of nitrogen requirement) were studied. The results for both cultivars showed that the CERES-Maize model underestimated crop yield (0 ≥ MBE). The amount of error for simulating yield of double cross cultivar 370 and single cross cultivar 260 was 1.24 and 0.44 tons per hectare, respectively. The accuracy of CERES-Maize model for simulating these two cultivars was in the category of good (NRMSE = 0.13) and excellent (NRMSE = 0.06), respectively. The error of CERES-Maize model for double cross cultivar 370 and for irrigation treatments was in the range of 0.89-1.65 t/ha and for fertilizer treatments was in the range of 0.43-9.9 t/ha. No difference was observed between the model errors in two fertilizer applications. Therefore, the model was not sensitive to fertilizer apportionment, while changes in irrigation water and fertilizer amount had a great effect on its accuracy. Based on all the results, the CERES-Maize model is recommended for simulation of both corn cultivars, although its accuracy was higher for the single cross 260 cultivar.

Due to increasing negative environmental impacts of storage dams, detention basins have been considered as an alternative solution instead of construction of concrete dams for reduction of flood damage in recent decades. Literature reviews on detention basins show that there are limited numerical and experimental studies in this regard. In this research the effect of bed roughness height of main channel on the inflow discharge has been investigated by considering the effective factors on the efficiency of basins in laboratory of Gorgan University in 2018.  Also, the general ability of using steady flow equations for unsteady flow was investigated by calibrating some of the discharge coefficient equations. For simulation, a metal tank was connected to a channel through a broad side weir. Three bed roughness heights of the flume in the range of 0.06 to 15 mm as well as three lengths of side weirs in the range of 30 to 90 cm were utilized. The experiments were carried out under unsteady flow condition by three inflow hydrographs with peak discharges of 17, 25 and 33 l/s and a base time of 504s. The flow depth was measured instantaneously by transmitter and the average inflowing discharge into the detention pond was calculated. According to the results, use of lateral flow discharge coefficient relationships of the steady flow cannot be generalized for unsteady flow. So, based on dimensional analysis of the Π-Buckingham theory, some relationships have been proposed for calculation of the average inflow rate into the detention pond and also for filling time of the pond. The maximum errors for calculation of the average inflow rate as well as the filling time of the basin were less than 15 percent. Also, increasing the streambed roughness can increase the average inflow rate to the detention basin by 25 percent.

The present study investigated and compared the effects of two types of crop residues, sugarcane bagasse (BG) and rice straw (RH), on some soil chemical properties and enzymatic activities related to the carbon and phosphorus cycle in sodic vertisols (control, C). The pot experiment was conducted in four replications using the factorial structure in complete randomized design in the greenhouse of Zanjan University in 2016. The factors of this experiment included two type of organic amendment (BG and RH), three application rates (L1=1.25%, L2=2.5% and L3=5% by weight) and four incubation times (two (M2), four (M4), eight (M8) and twelve (M12) months). Some of the most important chemical and biological properties were measured after the treatments. The results showed that values of soil organic carbon (SOC), carbon to nitrogen ratio (C:N), and available phosphorus (AP) were significantly (p< 0.001) affected by the type of organic amendments, their application rate, and incubation time. The highest and lowest SOC values were measured in the BGL3M2 and RHL1M12 treatments, respectively. Changes in SOC and total nitrogen (TN) were increasing by increasing the amount of organic amendments and decreasing by increasing incubation time. Total nitrogen in the RHL3M12 treatment increased 51.6% compared to the RHL1M12 treatment and decreased 8.5% (p<0.01) compared to the RHL3M2 treatment. AP in BGL3M12 treatment had a significant increase of 21.5% compared to BGL2M12 treatment. The highest alkaline and acid phosphatase activity was related to RHL3M12 (18.6 µg PNP g-1h-1) and BGL3M12 (7.1 µg PNP g-1h-1) treatments, respectively. RHL3M12 and BGL1M2 treatments showed the highest and lowest beta-glucosidase activity, respectively, and showed a significant difference of 87.5% and 70.3% with the control treatment. The highest and lowest levels of microbial biomass carbon (MBC) were related to RHL3M2 (71.5 mg kg-1) and BGL1M12 (28 mg kg-1) treatments, respectively.

Soil organic carbon is a key element in determining soil quality, health, and fertility. Due to the complexity of the structure and relationships of soil organic carbon pools, the use of models is beneficial in identifying the reaction of these pools to the change in ecosystem conditions. So, by using the RothC model, the effect of global warming and climate change on the amount of soil organic carbon pool of the agricultural ecosystem of southeastern of Mashhad was investigated. Therefore, the model was calibrated and validated using data measured in 2020 and available long-term data. Comparing the measured values of soil organic carbon and the simulated values by the model, the coefficient of determination (R2) was 0.89. Root means square error (RMSE): 3.45, mean difference (MD): 1.84, mean absolute error (MAE): 2.79, and model efficiency (EF) was 0.73, demonstrating the validity and suitability of the model. The modeling of the future climate changes of Mashhad showed a decrease in rainfall and an increase in temperature and evaporation, leading the amount of total soil organic carbon (TOC) would decrease by 1.13% compared to the current conditions. Considering the decomposition rate constant of the model's four active carbon pools, humus exhibited the slowest decomposition rate of 0.96%. At the same time, decomposable plant materials (DPM), resistant plant materials (RPM), and microbial biomass (BIO) were decreased by 1.18%, 2.21%, and 2.10%, respectively, compared to the current climate condition. Moreover, over time, the decomposition rate decreased due to the decay of active organic matter pools that are easily decomposed.

The integrated maps of soil moisture having high spatial resolution and appropriate quality are of great importance in land management. Due to the lack of monitoring stations in watersheds, especially in mountainous areas, field monitoring of soil moisture is a time-consuming, costly and error-prone process. SWAT model was used to obtain a suitable method for spatial and temporal simulation of soil moisture in the Marghab watershed of Khuzestan province with an area of 690 km2. The daily meteorological data of Barangard and Izeh synoptic stations, soil and land use maps, and digital elevation model were used as inputs to the model. The SUFI-2 program was used for calibration, sensitivity and uncertainty analysis, and validation of the model using the runoff data of Jologir-Marghab hydrometric station. The model was run from 2003 to 2019 for calibration and from 1995 to 2002 for validation, with a three-year warm-up from 1992-1994. Nash-Sutcliffe efficiency (NSE) and determination coefficient (R2) were used to determine the goodness of fit of the model, and P-Factor and R-Factor indices were used to determine the degree of uncertainty. Based on the simulated and observed monthly runoff hydrographs as well as the statistical criteria, the SWAT performance in simulating monthly runoff was acceptable both in the calibration and validation periods. The NSE, R2, P-Factor, and R-Factor were 0.76, 0.73, 0.68, and 0.62, respectively in the calibration period, and 0.73-0.71-0.60 and 0.65, respectively in the validation period. After model calibration and validation, soil moisture maps were obtained for the 1995-2019 period. The results indicated that SWAT model is a promising tool for simulating soil moisture in the catchment area with appropriate spatial (sub-basin scale and hydrological response units) and temporal (monthly and annual scale) distributions.

Effective and timely drought monitoring can contribute to the development of drought systems and the optimal management of water resources using these systems in turn can minimize the costs of drought. The purpose of this study is to investigate the drought using Landsat satellite data and meteorological and agricultural drought indices in three regions with different climatic conditions (Birjand, Shiraz and Rasht). For this purpose, drought indices based on satellite data including Normalized Difference Vegetation Index (NDVI), Soil Adjustment Vegetation Index (SAVI) and Simple Ratio (SR) were extracted from Landsat images for the period 2002, 2014 to 2020. Then the results of these indices were compared with the values of standard precipitation index (SPI) and Reconnaissance Drought Index (RDI). The study of indicators shows that the amount of indicators is high in all studied years in Rasht region. In Shiraz region, a significant decrease in the average value of indicators occurred in August and September from 2015 to 2020. Also, this decrease was seen in the average value of indicators in Birjand region from September 2002 to 2020. On the other hand, among the studied months, September 2015 in Rasht and Shiraz regions and 2014 (September) Birjand had the most drought in terms of remote sensing indicators. The results showed that in all three regions, remote sensing indices including NDVI and SAVI have a high correlation with SPI and RDI indices. The RDI index is superior to the SPI index for drought monitoring and prediction. As a result, the RDI index takes into account evapotranspiration in addition to rainfall and is more sensitive especially in dry areas such as Shiraz and Birjand where evapotranspiration is higher than rainfall.

Biochar application has been proposed as a suitable amendment for soil properties to sustainable agriculture. However, its effect depends on biochar application and soil properties. The purpose of this study was to investigate the sole and combined effects of different sources, rates and particle size of biochar on soil bulk density, aggregate stability, penetration resistance and shear strength in a silty clay loam soil. Palm leaf and lemon peel biochar prepared at a pyrolysis temperature of 500∘C with three particle sizes 2–4, 0.8-2 and smaller than 0.8 mm, mixed in the soil at application rates of 0, 0.5, 1, 2 and 4 % (by weight). After 15 months (2019 to 2020) incubation at standard condition in research glasshouse of faculty of agriculture Shiraz University, soil properties analyzed by standard methods. In general, the applied biochar decreased soil bulk density, penetration resistance, and shear strength and increased aggregate stability significantly, as compared to the control. Palm leaf biochar played better roles in improving soil properties as compared to lemon peel biochar. On average, by increasing biochar rates to 4%, the soil bulk density, penetration resistance and shear strength decreased by 14.5, 85 and 59.8% respectively and the aggregate stability increased by 50.5%. The greatest effect of biochar was obtained at particle size smaller than 0.8 mm for aggregate stability and penetration resistance. Bulk density and tension strength of the soil did not considerably change using different particle size of biochar. Base on the results, improvement in soil physical properties can be accomplished by application of appropriate particle size and rate of different sources of biochar.

Sand production is a process affecting the well performance significantly. It would make severe challenges to water depletion and pump operation. Due to high drilling costs, identifying the sand production phenomenon prior to locating a new well is highly important. Land subsidence would occur in areas with fine-grain aquifers as a consequence of high water depletion. Furthermore, the sand production phenomenon is also related to the fine-grain aquifer. First, the land subsidence was estimated in Alborz province using Sentinel-1 satellite images from 2017 to 2021. Then, the relationship between sand production and the land subsidence phenomenon was investigated using the existing field evidence in southern parts of Savojbolaq. Also, the information on some wells with sand production phenomenon was compared with land subsidence maps. Finally, the Jaccard coefficient was employed to identify the similarity between land subsidence and sand production maps. The results indicated that the maps were identical at the level of the Jaccard coefficient of 65%. In other words, the wells in the high-risk land subsidence area could suffer from the sand production phenomenon with a probability of 65%.

Considering the importance of water in the agricultural sector, it is necessary to know the usable or effective amount. Therefore, in this research, using remote sensing and implementing the Surface Energy Balance Algorithm (SEBAL) on 28 images from Landsat 8 for the crop years 2014 to 2022 in During the growth period of dry wheat in fields of Khomein city, the rate of evapotranspiration and effective rainfall were estimated. The accuracy of SEBAL has been evaluated with Penman-Monteith and pan evaporation methods, and then the results obtained with experimental methods of effective rainfall estimation have been compared and their relative error (RE) has been estimated. The results showed that the USDA method with a RE of 12.2% had the lowest error and the FAO with a RE of 60% had the highest error compared to the SEBAL.

Cavitation is one of the failure factors of spillways, which requires risk zoning to control this phenomenon. In this research, to obtain a method for zoning the risk of cavitation, the spillway information of Surk dam in Chaharmahal Bakhtiari province  was used. In the modeling process, first, the geometric model of the overflow was constructed and after meshing and applying boundary conditions, flow analysis was done. The cavitation index was calculated in 18 sections according to the values of flow velocity and height, chute slope, and other necessary parameters. The results of Flow-3D software for qualitative assessment of the cavitation risk situation in Surk dam spillway are of appropriate accuracy; Thus, the RMSE error of pressure 0.26×10-2 pascal and velocity 0.23×10-2 m/s was obtained compared to the laboratory results. Also, parameters affecting the reduction of cavitation such as roughness and aeration were investigated. The results showed that there is a possibility of cavitation and damage caused at a distance of 70 to 95 meters from the crest of spillway. The results of the sensitivity analysis showed that the use of a uniform roughness of 2.5 mm and aeration during the chute increases the cavitation index. This roughness moves the cavitation areas to the downstream sections of the spillway. Also, by creating a roughness of 1.5 mm in two end sections 99.75 and 105  meters from  the crest of spillway, the results of the nearest neighbor algorithm (NNA) showed a more critical state than the Flow-3D model. By applying a roughness of 2.5 mm, in the two end sections of 42 and 89.25 meters from the crest of spillway, the NNA showed a more critical state than the Flow-3D model, which means that these areas are more vulnerable to the cavitation phenomenon.