فصلنامه حفاظت منابع آب و خاک
سال یازدهم شماره 4 (پیاپی 44، تابستان 1401)

The optimum and sustainable use of soil is only possible with a correct and complete understanding of its properties. This study was conducted to investigate of should be omitted some physical, chemical, and micromorphology properties of soil formed on the should be added landscapes in the should be added semi-arid region of Lorestan province.

For this study 5 profiles, a long, a should be omitted transect in the direction of slop (plateau, gravelly colluvial fan and pediment plain) were identified in the area, and divided into different physiographic unit based on the type of parent material. At each, physiographic units a profil was excavated, dissected sampled. Physicochemical together with micromorphological observations performed and Soil Taxonomy and WRB were used for soil classification.

The results showed that should be omitted low amounts of organic carbon in the surface and depths of the soil due to the high rate of decomposition of these substances in these areas. The equivalent calcium carbonate in all profiles showed an increasing trend with depth. Thin section investigation showed that there was not much difference between the horizons in terms of the type of voids and the relative distribution of large particles (C/F). Planar, vugh and cannel voids were the most dominant voids of the profiles. Accumulation of calcite was seen in various forms such as coating, infilling, needle- shaped calcite, and nodules calcite which indicates that secondary calcite, and caused a should be omitted crystallite calcite of kind b-fabric. Soil structure in most the horizons of kind angular blocky and sub angular blocky. Also, lenticular plates gypsum was observed on the Bky horizon on the of the gypsum physiographic units plateau. According According to the existence of pedogenic gypsum in the soils of the region, and the important of the gypsum horizon in the soil classification system and its important effect on plant growth. New great groups, and subgroups of Xerepts (Gypsic Calcixerepts) are suggested to be included in soil taxonomy.

Results of the study showed raise in topography and parent material on the variability of soil properties in the area.

Heavy metals are considered as one of the important factors threatening water quality in many areas, so they have attracted the attention of many researchers. In this research, the temporal variations of arsenic removal from aqueous solution were investigated using a photocatalytic-adsorbent composite TiO2-Fe2O3 for 360 minutes. Then the capability of linear and nonlinear models of Pseudo-first-order, Pseudo-second-order, Elovich, and power was evaluated in describing the kinetic process.

To determine the kinetics of arsenic adsorption from aqueous solution by TiO2-Fe2O3 composite, batch adsorption experiments were performed at pH 7, initial concentration of 40 mg/L, and adsorbent dose of 1 g/L. After linear and nonlinear fitting of the kinetic models on the adsorption data and finding the constants of each model, the adsorption amount was calculated at different times and compared with the values measured in the laboratory.

The results showed that the amount of adsorption increased with increasing time, and the concentration of arsenic reached equilibrium in 25 minutes. Considering the values of error functions in linear fitting, it was observed that the Pseudo-second-order equation with the lowest errors and the highest coefficient of determination (99.92%) was better agreed to the laboratory data. After this model, the Pseudo-first-order equation performed better, and the weakest simulation was for the Elovich model. In nonlinear analysis, the coefficients of determination of all models were high and very close to each other, so according to the values of error functions, the Elovich model had the lowest error value, which indicates the best fit on the kinetic data. Afterward, power, Pseudo-second order, and Pseudo-first order models had the best fit on the kinetic absorption data, respectively.

Comparison of models up to 360 minutes using the coefficient of determination and error functions (R-squared, root-mean-square error, sum of squares error, sum of absolute errors, absolute relative error, HYBRID, Marquardt’s percentage standard deviation), showed that the use of linear shape can lead to a completely different result and interpretation than the nonlinear shape of the model. So that based on linear methods, Pseudo -second-order model and based on nonlinear methods, the Elovich model had the best fit on kinetic data. Nonlinear fits of kinetic models were also superior to linear forms. In general, the results showed that TiO2-Fe2O3 composite has a high potential for arsenic removal from an aqueous solution.

Drought as a natural hazard significantly impacts various sectors such as agriculture and water resources and causes considerable damage to these sectors worldwide. Therefore, adaptation strategies should be taken to reduce drought damage, and in the meantime, planning and adaptation to drought conditions using drought forecasting is one of the most effective strategies. Due to the need for drought forecasting and the limited studies that evaluated drought indicators obtained from the rainfall forecast output from the North American Multi-Model Ensemble (NMME) in Iran. This study evaluated these models in four catchments of Karkheh, Karun, Heleh, and Hindijan-Jarahi for1982-2018.

In this study, the monthly output of different NMME ensembles were evaluated in the forecast leads of 0 to 9 months from 1982 to 2018, the SPI drought index was calculated. Comparison of these data with GPCC data was used for evaluation. Three quantitative criteria, including correlation coefficient, RMSE, and BIAS, were used for evaluation. Also, to integrate the existing models, two

a: Arithmetic mean between the existing models and B: Weighted average between the models have been evaluated by considering the correlation coefficient (CC) results. Also, two criteria (i.e., POD and FAR) and the quantitative statistical criterion (i.e., correlation coefficient) were used to evaluate the SPI drought index.

The results of the precipitation evaluation of the models showed that the integrated models have better performance than the individual models. In this integrated model, the weighted model also had better performance. Evaluation of spatial distribution of precipitation models also showed the excellent performance of NMME models in Karun and Hindijan-Jarahi catchments in the zero-month forecast lead and Hindijan-Jarahi catchments in the one-month forecast lead. The results of drought index evaluation showed that integrated models, although having better performance in precipitation forecasting, but in drought forecasting, the best performance belongs to NASA-GMAO-062012 and CFSv2 models. The results also showed that drought index forecasts in three and six-month periods have better performance than one month. Spatial distribution evaluation also showed that the models perform better in the southern basins. In general, it can be concluded that NMME models have good performance in predicting drought in some places and specific forecast leads, so they should be evaluated at each point before use.

The results of precipitation evaluation showed that, in general, integrating the output of dynamic models increases its proficiency, and integration in weighted mode (WeightedNMME) performs better than the non-weighted model (NMME). According to the zero-month forecast among individual models, the NASA-GMAO-062012 model has the most skills in terms of the correlation coefficient. However, in the one-month forecast lead in terms of the correlation coefficient, RMSE and BIAS, the best performance belongs to the CFSv2 model. Evaluation of drought indices showed that the model's performance could be different from their performance in predicting rainfall. WeightedNMME, for example, performed well in NASA-GMAO-062012 and CFSv2 months, although they performed well in predicting drought. The spatial evaluation also showed that the southern catchments perform better than other basins.

Evaluation of damages caused by flash floods in urban areas is one of the most important concerns after their occurrence. Like other urban elements, vehicles suffer damage that should be considered for managing urban floods. The damage values ​​in most car damage models in flood are presented with the assumption of their stability and purely in terms of depth, while the flood velocity as flood important variable is also effective on the severity of damages. In the present research, while evaluating the eight theories presented in the Australian Rainfall and Runoff Guideline (AR&R), an attempt is made to provide the best theory to provide a simple and accurate algorithm for determining the damage to sedan vehicles in flood as a function of depth and velocity.

To achieve the goal of this research, the formulas and diagrams of the stability limit of eight theories presented in AR&R were evaluated and after deleting one of the theories and modifying the Melbourne Water presented in 1996 and DPW presented in 1986 formulas, the stability map of the sedan cars in Darwaze Quran flood on March 25, 2019 in Shiraz was produced for seven theories proposed in this guideline. Subsequently, by combining the stability limits of each theory with the HAZUS-MH depth-damage diagram and zoning below the stability limit diagram as stable areas, the risk map algorithm for stable sedan vehicles was provided separately for each theory. In the following, the risk map in the adjacent parking area of ​​the water pool upstream of the Quran Gate of Shiraz for each theory and, with their help, the total damage for the Pride_131 was calculated as a common vehicle in Iran. Finally, the measures of maximum damage, as well as the total damages were obtained from each one.

One of the main and most important results of this research is providing an algorithm for determining the damage of the sedan vehicles in a certain range of depth and speed for each of the theories proposed in AR&R, which were used for producing the risk map. In addition, the total damages for the Pride-131 as an index car were calculated by the proposed algorithms. The minimum damage was obtained by using AR&R (1987) theory algorithm equal to 10 billion and 265 million toman and the maximum amount of damage was obtained by using achieved by using AR&R (2011) theory algorithm equal to 14 billion and 32 million toman.

It was found that the use of the depth of flood as a hydrostatic index, which is now the criterion for calculating car damage, is not accurate enough and it is better to use velocity and depth composition as a hydrodynamic index for this purpose. At the same time, it was proved that among the other theories, the relation and the final limit of stability presented in the AR&R (2011) theory to provide a model for determining the damage to small and light vehicles in the flood as a function of depth and velocity has better and more reliable results. It is worth noting in order to achieve the more accurate damage amounts of vehicles in flood, more theoretical and experimental studies considering different types of vehicles are essential.

Landslide is one of the natural hazards that lead to a lot of human and financial losses. Researchers on the subject of landslide susceptibility are investigating the possibility of landslides with respect to topographic and geo-environmental conditions, and the obtained information is critical in landslide risk management Preparation of landslide sensitive points is an essential tool for assessing landslide risk and is very useful in better planning and management of these areas. In this research, models based on artificial intelligence and two statistical variables in determining landslide sensitive points in West Azerbaijan province have been studied and compared.

Methods based on artificial intelligence and two statistical variables were used to prepare landslide-sensitive points in the province of West Azerbaijan, which is located in northwestern Iran. This study was conducted in four stages. The first stage: the study of landslides in the studied region based on the database of the Forests, Rangelands and Watershed Organization of Iran (FRWO) and the identification of 110 landslides through field surveys, interpretation of aerial photographs and Google Earth satellite images, the second stage: data collecting and creating a spatial databases of effective factors, the third stage: applying the Frequency Ratio (FR), Shannon Entropy (SE), Bagging (BA), Random Forest (RF) and hybrid model (RF-BA) and stage four: methods validating using the system performance curve (ROC). Based on field surveys and similar studies, 12 factors affecting landslide occurrence including altitude, slope angle, slope direction, distance from fault, distance from river, distance from road, drainage density, road density, rainfall, soil, land use and lithology were identified. In the field survey, 110 landslides were identified in West Azerbaijan. 70 percent of the data were randomly selected and used for modeling and 30 percent of the data were used for validation.

In terms of geographical directions, the southern direction with a weight of 1.49 had the greatest impact on the occurrence of landslides in the province. The least weight was related to flat areas where no landslide occurred. The results of slope factor showed that the middle slopes had the greatest effect on the occurrence of landslides, so that in low slopes due to low gravity, less landslides occur and too much slopes were related to mountainous areas that were covered with rocks and there was very thin soil that is not suitable for landslide. The study of land use factor showed that 48 percent of landslides occured in agricultural areas. The results showed that most of the landslides occurred near rivers and faults. Also, in some areas, the closest distances to the road had the greatest risk of landslide

The results of this study showed that the artificial intelligence models (RF and the combined model RF-BA) had the higher efficiency than the statistical models (FR and SE). The accuracy of the combined models was higher than the single models. The ROC curve results showed the accuracy of 0.92, 0.91, 0.89 and 0.88 with RF-BA, RF, FR and SE models, respectively.

The share of agricultural sector in West Azerbaijan province, Iran from surface water abstraction is 91.2%. Excessive consumption of water resources, drought and destruction of agricultural lands, dependence of work and livelihood of 1.2 million population of province on the agricultural sector, the adoption of long-term development plans, have intensified the water crisis in agricultural sector. Therefore, managing the consumption of surface water is essential. This study investigates the effect of agricultural, economic, policy-making, climatic, socio-cultural and educational scenarios on management of surface water consumption and availability.

The present study is an applied research has been carried out using the dynamic system approach in order to achieve sustainable management of water resources in West Azerbaijan province. First, the statistical data from the previous years (1991-2018) are entered into the system using Excel and SPSS software. Then, the dynamic system model is developed in VENSIM software. To ensure the efficiency of the model in evaluating policies, the model validation test is performed. After making sure that the model works efficiency, population growth scenarios with different fertility rates, PS1 (2.11 children), PS2 (1.95 children), PS3 (1.5 children) and PS4 (2.6 Children) and climatic scenarios with a probability of 20% annual rainfall reduction, continuation of the current rainfall trend and 20% annual rainfall improvement are introduced to the model. Additionally, investment scenarios with the aim of 0.5% improvement in the annual irrigation efficiency and the crop scenario such as low-consumption cultivation pattern are introduced to the model. Moreover, the impacts of participatory and education scenarios, are simulated up to the horizon of 2051.

The results showed that in the validation test there is a high correlation between the simulated values ​​and the observed values ​​of surface water, and the model is effective in evaluating policies. Upon continuation of the rainfall current trend, available surface water decreases by 3.76% during the simulation period (2018-2051). Droughts intensify with a decrease of 20% of annual rainfall due to the direct effect of rainfall on runoff, available surface water is reduced by 20.54%. The largest decrease in the surface water content is related to the scenario of reduced rainfall (-20%) together with the scenario of increasing fertility rate (ps4), which cause a decrease in the available surface water by 46.15%. Population growth has increased the total water consumption and even agricultural water demand by 27% over the 2051 horizon. Simultaneously using low-consumption cultivation pattern scenarios, investment to improve irrigation efficiency, participatory activities and training programs to improve water management, water consumption decrease by 29.20% and agricultural water demand from the surface water decrease by 31.37% under these conditions, the available surface water improves by 9.12% to the horizon of 2051.

The results showed that, it is necessary to review population scenarios at the national level. Also, the agricultural scenario of observing the pattern of low-consumption cultivation with reducing water demand per hectare is known as the best scenario and its application has increased the available surface water by 2.45 times.Keywords: Water resource management, Economic/policy-making scenarios, Climatic scenarios, West Azerbaijan province

The share of agricultural sector in West Azerbaijan province, Iran from surface water abstraction is 91.2%. Excessive consumption of water resources, drought and destruction of agricultural lands, dependence of work and livelihood of 1.2 million population of province on the agricultural sector, the adoption of long-term development plans, have intensified the water crisis in agricultural sector. Therefore, managing the consumption of surface water is essential. This study investigates the effect of agricultural, economic, policy-making, climatic, socio-cultural and educational scenarios on management of surface water consumption and availability.

The present study is an applied research has been carried out using the dynamic system approach in order to achieve sustainable management of water resources in West Azerbaijan province. First, the statistical data from the previous years (1991-2018) are entered into the system using Excel and SPSS software. Then, the dynamic system model is developed in VENSIM software. To ensure the efficiency of the model in evaluating policies, the model validation test is performed. After making sure that the model works efficiency, population growth scenarios with different fertility rates, PS1 (2.11 children), PS2 (1.95 children), PS3 (1.5 children) and PS4 (2.6 Children) and climatic scenarios with a probability of 20% annual rainfall reduction, continuation of the current rainfall trend and 20% annual rainfall improvement are introduced to the model. Additionally, investment scenarios with the aim of 0.5% improvement in the annual irrigation efficiency and the crop scenario such as low-consumption cultivation pattern are introduced to the model. Moreover, the impacts of participatory and education scenarios, are simulated up to the horizon of 2051.

The results showed that in the validation test there is a high correlation between the simulated values ​​and the observed values ​​of surface water, and the model is effective in evaluating policies. Upon continuation of the rainfall current trend, available surface water decreases by 3.76% during the simulation period (2018-2051). Droughts intensify with a decrease of 20% of annual rainfall due to the direct effect of rainfall on runoff, available surface water is reduced by 20.54%. The largest decrease in the surface water content is related to the scenario of reduced rainfall (-20%) together with the scenario of increasing fertility rate (ps4), which cause a decrease in the available surface water by 46.15%. Population growth has increased the total water consumption and even agricultural water demand by 27% over the 2051 horizon. Simultaneously using low-consumption cultivation pattern scenarios, investment to improve irrigation efficiency, participatory activities and training programs to improve water management, water consumption decrease by 29.20% and agricultural water demand from the surface water decrease by 31.37% under these conditions, the available surface water improves by 9.12% to the horizon of 2051.

The results showed that, it is necessary to review population scenarios at the national level. Also, the agricultural scenario of observing the pattern of low-consumption cultivation with reducing water demand per hectare is known as the best scenario and its application has increased the available surface water by 2.45 times.

Surface soil moisture is an important variable in nature's water cycle and can be affected by various factors, including temperature and soil characteristics. The use of ground sensors for measuring moisture can lead to spending time and expense and inappropriate distribution of samples on large scales. Therefore, Remote sensing observations can be an important tool in estimating soil moisture. The present study aims to use the TOTRAM model using Landsat 8 images and the SVR method using Sentile 1 images to estimate soil moisture.

In the present study, two TOTRAM methods based on pixel distribution in LST- VI space and the SVR method were used to extract soil moisture using the SAR technique and Sentinel 1 data. To implement the TOTRAM method, Landsat 8 images related to 4/29/1398 and 5/30/1398 are downloaded and after extracting NDVI and LST maps, The correlation between the dependent variable of moisture and independent temperature variables and vegetation variables has been investigated using Geographically weighted regression (GWR). To implement the SVR method after acquiring Sentinel 1 images related to 31/05/1398 and 27/04/1398, Soil Moisture Data Product FLDAS and 500 meters product of Modis Satellite (MCD12q1) were called to classify land cover in the Google Earth Engine system, and maps related to soil moisture were extracted. After extracting the moisture maps the distribution of moisture using the local Moran index has been investigated. By defining this index, positive values ​​for this index represent the cluster of distribution.

Examination of the soil moisture map obtained by the SVR method showed the concentration of moisture in areas with vegetation and water and the change in moisture status from July to August was visible. The humidity pattern has shown the reflection of the precipitation pattern so that maximum precipitation and humidity were observed in April and in summer both precipitation and humidity components decreased. Examination of the TOTRAM method and application of the GWR method has shown a complete correlation between NDVI LST and moisture. However, the correlation between LST and humidity with B (values) and standard error (SE) of 0.995 and zero corresponding to July and 0.981 and zero corresponding to August showed the highest correlation with vegetation variable with moisture dependence parameter, which this correlation In August, with increasing the coefficient of determination of R2 to 0.997 and a significant decrease of NDVI to the value of 0.415 in July, it has increased much more. Application of Moran local index with values ​​less than 0.05 for p-value and positive values ​​for z and near positive number 1 for Moran index showed the cluster distribution of moisture variable.

The results of TOTRAM and SVR methods showed the dependence of soil moisture status on conditions and cluster moisture distribution. According to the correlation coefficients of geographical regression, there is a greater correlation between temperature and humidity variables, especially in August, due to the decrease in vegetation density. The results of the SVR algorithm maps showed that in areas with the presence of vegetation, especially dense vegetation, we see an increase and with increasing temperature, we see a decrease in humidity. Also, the coordination of moisture patterns of the SVR algorithm and precipitation showed a direct relationship between moisture and precipitation. Considering that the SVR method uses parameters such as radar scattering intensity and land cover classification, as well as the use of Sentinel 1 radar images by this algorithm, more accurate results can be expected from this algorithm.

Due to the importance of determining the behavior of plants in water shortage conditions, it is necessary to determine the coefficients of plant sensitivity to water at different stages of plant growth. Water shortage in the Sistan plain is a serious and important issue, so that the lack of water supply to the Helmand border river, which is the only source of water supply in the region, leads to the destruction of agriculture. The low possibility of increasing new water resources and the need to increase agricultural production from limited water resources in this region requires the use of appropriate scientific and technical methods to increase the productivity of agricultural water consumption. Also, changing the pattern of cultivation and replacement of high-income crops in recent years in arid areas has received much attention from farmers. The purpose of this study is to investigate the effects of water stress on the behavior of garlic in dehydrated conditions to calibrate the production function of this plant to obtain yield response coefficients in conditions of water shortage.

In this study, garlic plant was implemented in the form of a randomized complete block design with three replications in 1398 and 1399 in Sistan and Baluchistan province. Treatments were applied based on stress levels compared to control 30, 35, 40, 45, 50, 60 and 70% of water requirement. This station is located 20 km southeast of Zabol city at latitude 61 degrees and 41 minutes and latitude 30 degrees and 54 minutes in Sistan region. The altitude of the station is 483 meters above sea level, the average annual rainfall is 55 mm and the annual evaporation rate is 4500 to 5000 mm. This region has a very dry climate with very hot summers and mild winters. The area of ​​the experimental plot was equal to 750 square meters was selected for planting garlic of Chinese cultivar before planting the bed was reinforced with animal manure and after growing based on soil fertilizer test, 300 kg of potassium phosphate, 200 kg of triple phosphate and 100 kg. One kilogram of urea was added to the soil. Then plow and disc and finally leveled. On the 24th of October, grooves were planted with a shovel at a distance of 20 cm and a depth of 5 cm to plant garlic of Chinese cultivar, and garlic tubers were planted at a distance of 8 cm on the rows. Then, based on the obtained information, the two functions of Tafteh and Raes production were investigated and yield response factors were determined during the growing period.

Comparing the two methods used the highest yield of garlic in the study was 8240 kg / ha, for the production of which 833 mm of water was used. The data of the first year were used to calibrate the yield response factors of garlic and after calibrating the two production functions of Raes and Tafteh, the values ​​of the yield response factors of garlic were presented. This coefficient was different in the range of 0.5 to 1.2 in different periods of garlic plant growth, the change curve was presented. Evaluation of Rice method in the second year with root mean square error of 1302 kg / ha and normalized value was about 23%.

In the method of Tafteh the mean root mean square error was 485 kg / ha and the normalized value was about 11%. Therefore, the method of Tafteh is recommended in determining the yield of garlic under water stress conditions.

Dam measurement and behavior assessment is a new issue that can be due to changes in available parameters to develop a model examining the behavior of individual parameters on the dam as well as on each other and analyze the changes and create the necessary policies. This study aims to propose a hybrid method involving logistic regression with particle swarm optimization algorithm with real value to predict the behavior of dam equipment.

In this study, from 365 days data, from 04/20/2018 to 04/20/2019, of which 600 sets of dam equipment data including parameters of water temperature, water level, valve pressure, sedimentation rate, pore pressure, air temperature, inlet water volume, specific dam characteristics, concrete conditions, reservoir water level, horizontal and vertical displacement, transmission connection components and ground acceleration, strength, pressure, tensile and high stress were used for modeling. Real value-logistic regression and 120 datasets were used for modeling the should be added of particle group optimization algorithm. To evaluate the performance of the proposed method, four statistics including coefficient of determination (R2), root mean square error (RMSE), scattering coefficient (SI), and means bias error (MBE) were used.

The results showed that the model has an acceptable performance in predicting piezometric pressure in the dam body. Also, the results of the artificial neural network model show acceptable convergence with R2 = 0.930 and SSI = 8.587. The results related to the training data of the model also indicate that the mean (µ) and standard deviation (σ) of the proposed model are equal to 1.341 and 1.526 for the training data and these values for the validation data are equal to 1.576 and 2.247, respectively indicating the good performance of the proposed model. In the cumulative probability criterion, the proposed model with P50 = 0.940 and P90 = 1.742 indicates that the results are acceptable.

The results indicate that the real value-logistic regression particle swarm optimization implements the principle of structural risk reduction instead of minimizing the experimental risk that provides excellent generalization for small sample sizes. The ratio of predicted piezometric values ​​to read values ​​for about 72% of the data in this model is about one, indicating the appropriate training and predictive power of this model. Finally, according to the evaluation criteria, the hybrid model performs better than the presented methods.