نشریه مدل سازی و مدیریت آب و خاک
سال سوم شماره 2 (تابستان 1402)

Groundwater is an essential natural resource that is widely used to meet domestic, industrial, and agricultural needs. In recent years, the amount of withdrawal from groundwater has been more than the amount of its recharging leading to going out of balance. Since groundwater is in the ground and it is not possible to observe directly, identifying its properties is time-consuming and expensive. On the other hand, problems such as inconsistent and incomplete input information, heterogeneous aquifers, etc., have made groundwater study difficult. In many watersheds, groundwater resources are the strategic and primary source of water supply for different users. However, groundwater extraction has exceeded the amount rate of recharge in many regions around the world, resulting in harmful ecological and environmental problems, such as water level decline, water quality degradation, drying up of wells, increased pumping costs, and land subsidence. Assessing groundwater resources for their available water volume and obtaining an accurate prediction of groundwater levels (GWL) is central to sustainable management (i.e., balancing between demand and supply) of groundwater and surface water resources in a watershed. Therefore, tools such as groundwater modeling are used to solve this problem. Simulation of groundwater flow with mathematical models is an indirect approach to solving problems with lower costs than direct methods. In fact, the use of mathematical modeling is to simulate the natural conditions of the water surface with mathematical relationships. Groundwater modeling is done using differential equations, and one of the most widely used methods in solving groundwater problems is the use of finite differences and finite elements. Accordingly, the groundwater modeling system (GMS) model and the MODFLOW code were used in this research to study the Mahabad aquifer. Next, the trend of changes in the groundwater level of the range was analyzed by non-parametric tests. Accordingly, the groundwater modeling system (GMS) model and the MODFLOW code were used in this research to study the Mahabad aquifer.

The study area of Mahabad is located in West Azarbaijan province. GMS software and MODFLOW code were used for groundwater simulation. Using the information of 22 observation wells, exploitation wells information, river information, recharge, and withdrawal from groundwater, the desired model was built. . The model was run in September 2015 for the steady-state and October 2010- September 2011 for the transient state with a monthly time step. The values for hydraulic conductivity and storage coefficient were calibrated for the steady and transient states, respectively. Aquifer thickness varied from 60 to 200 m, and the cell size was considered 200 × 200 m. Rainfall infiltration, return flow, and input flows feed the aquifer. Seventeen percent of the monthly rainfall was considered rainfall infiltration that fed the aquifer. Moreover, based on the wells' primary use, return water from the wells was considered about70, 75, and 20% for drinking water, industrial and agricultural wells, respectively. The GWL is higher in the South part of the aquifer compared to other parts and, as we move from the South part of the aquifer towards its central and southern regions, the GWL declines. In conclusion, the groundwater flows from the upper South part of the aquifer towards its lower part. More exploitation wells are in the aquifer's central section, and most of their extracted water is used for urban and agricultural purposes. It was then implemented in two stable and unstable modes and its performance was evaluated with root mean square (RMSE), mean absolute error (MAE), and coefficient of determination (R2) criteria. Various statistical methods have been provided to analyze the trend of time series. Among them, non-parametric methods are more useful in the time series of hydrological variables. These methods are suitable for time series that have elongation or skewness and are independent of the statistical distribution of the time series.  In the following, the Mann-Kendall method and Sen’s slope were used to determine the trend of the groundwater level at significant levels of 90, 95, 99, and 99.9%.

The simulation results showed that there is a very good agreement between the simulation and observational data. The model evaluation criteria including RMSE, MAE, and R2 for two stable and unstable modes were calculated as 0.84, 0.63, and 0.99, as well as 0.88, 0.72, and 0.98 m, respectively. These values showed the appropriate efficiency of the model. Based on the results, the highest level of groundwater was in the south of the Mahabad aquifer and the lowest level was in the north of the aquifer.  The optimized values ​​of hydraulic conductivity, special yield, aquifer thicknesses, values ​​of exploitation wells, and aquifer transmissivity were determined from the groundwater simulation results. The results of the Mann-Kendall test showed that Haji Khosh, Gapis, and Gorg Tapeh stations had the highest downward trend. So, in these stations, the downward trend was more significant at the level of 0.99%. The Mann-Kendall Z-parameter values were positive for the Qom Qala station, which indicated the rising trend of the underground water level in this area. The results of Sen’s slope test also confirmed the results of the Mann-Kendall test. It was so that the Sen’s slope test showed that the downward slope of the three stations Haji Khosh, Gapis, and Gorg Tapeh occurs more strongly.

The results of this research showed that GMS and MODFLOW codes are suitable tools for simulating groundwater and the condition of the aquifer with proper accuracy. Also, the results of Mann-Kendall and Sen’s slope tests showed that out of 19 wells, almost 18 had a downward trend, which shows that the Mahabad aquifer is not in a favorable condition and with the increase in harvesting and decrease in rainfall, especially in recent years, its situation will worsen. The Mann-Kendall test showed that the Mahabad aquifer is in poor condition so out of the 19 investigated wells, approximately 18 wells had a downward trend in the groundwater level. The age slope estimator method also confirmed the Mann-Kendall results. Examining the obtained results exhibits that the use of new approaches for simulation provides the opportunity to manage and balance the allocation of groundwater resources effectively. Further, the use of new tools can be considered for implementing balancing scenarios related to groundwater resources.

Land use change reporting over time is necessary to assess and monitor the state of natural and agricultural resources. Knowing about the change of use is necessary to identify the priorities of public investment in the management of natural resources and to evaluate its effectiveness. The purpose of land change investigation is land use management. Among the management cases, we can mention the evaluation of the effect of economic activities and development on the environment. In such cases, these organized reports are the best sources of decision-making. Land use management can ensure that resources are used efficiently and people's future resources are preserved. This process is the main component of a development plan. Timely and accurate detection of land use changes is the basis for a better understanding of the relationships and interactions between humans and natural phenomena, and as a result, provides better management and more appropriate use of natural resources. Satellite images as a type of remote sensing data are well used in the field of natural sciences for quantitative and qualitative measurement of land cover changes. The construction of the Houzian dam in 2015 and also the expansion of mines and construction in the natural resources of Aligudarz city in Lorestan province and also the lack of accurate statistics on the amount of land cover/use changes in the region make such research necessary. In the present study, land use changes in Aligudarz city were investigated during nine years for the years 2012 and 2014 with the help of multi-spectral satellite images and the artificial neural network. In the structure of the artificial neural network, numerous nodes work together in parallel with the purpose of processing. Each node is a data structure. This data structure is placed in a communication network with each other and the network is taught by humans.

In this study, several key steps were used to prepare and identify LULC changes in Aligudarz city, which include data pre-processing, image processing and classification implementation as well as validation. The required images were selected among the available images in such a way that they have minimum cloud cover and maximum greenness in the plants and trees in the area, and the date of the images are related to the same month. This study uses land use change detection in the east of Aligudarz county using Landsat 8 OLI and TIRS images. The spatial resolution of these images was improved to 15 m using the fusion technique and panchromatic band. At first, preliminary pre-processing including radiometric, atmospheric, and geometric corrections were done on the raw data. The geometric correction was done with the RMS square root error of 0.22 pixels. Radiometric and atmospheric corrections were done in ENVI 5.3 software using Radiometric Calibration and Quick Atmospheric Correction tools. The artificial neural network method was used to prepare land use maps for 2013 and 2021. The neural network structure used in this research is a three-layer perceptron neural network, which includes seven input neurons (number of satellite image bands), eleven intermediate neurons, and six output neurons (number of land cover map classes). The classification accuracy was evaluated quantitatively by comparing the LULC classes obtained from the training phase with the data obtained from the testing phase. The classification accuracy was evaluated quantitatively by comparing the LULC classes obtained from the training phase with the data obtained from the testing phase. In this study, the points taken from the ground surface and Google Earth Pro 7.3.4.8642, using the error matrix and the Confusion Matrix Using Ground Truth ROIs tool were used. Detection of changes between two classified maps was done with Change Detection Statistics and Workflow Change Image and Spear Change Detection tools.

The results of this study showed that the artificial neural network has an acceptable performance in investigating land use changes and, The Kappa coefficient for 2013 and 2021 was 0.83 and 0.71%, respectively. Due to the construction of Houzian Dam in 2016, water areas have witnessed an increase of 1.34%. Also, the construction of the dam has led to an increase in the area under irrigated cultivation, so the area under cultivation in 2021 experienced an increase of 5.53% compared to 2013. In addition, the construction of the dam has caused the highlands to decrease by 4.30 %. Because the water of the dam has been used to irrigate the highlands where there was not enough water to irrigate them before the construction of the dam. The area of mines has increased by 0.23% during the studied period. The area of uncovered regions has decreased by 1.74% compared to 2013. Also, the area of habitation regions has decreased by 1.06% to 18.18 square kilometers in 2021.

The survey of the land use map of Aligudarz showed that the heights and water areas have the largest and smallest areas, respectively. The results of this study showed that in the years after the construction of Houzian dam compared to before its construction. The total area of water and total vegetation has increased. Since the construction of a dam in an area has short-term and long-term effects, it should be noted that the increase in vegetation and the cultivated area is considered a short-term effect. Therefore, it is necessary to investigate the impact of creating this water structure in the region's ecosystem in the long term by forecasting models. In the investigation of mines, the appearance of water areas indicates an increase in the depth of excavation. Since this city is an important center for stone production, the absence of a specialized regulatory body on the number of harvests and the impact of mining on the environment is felt in this region. Another part of the increase in water areas is due to the existence of errors in the classification of land use in the artificial neural network. In using the results of this research, it is important to mention that these results were obtained for the area of the dam and the increase in vegetation caused by the construction of the dam cannot be generalized to the entire basin.

Soil salinization is a global environmental problem with serious economic, social and economic consequences. Measuring soil salinity includes the concentration of all salts dissolved in the soil, generally expressed in units of electrical conductivity (EC). Determining where, when, and how soil salinity occurs is essential to determining the sustainability of land use and development systems. Due to the ability to repeat and capture a wide range of remote sensing images, this technology will be useful for detecting changes even in short periods of time, and as an essential tool in monitoring soil salinity, it provides very valuable information on the size of the captured pixels. Flooding from this rainy season can have a large impact on soil salinity. The purpose of this research is to extract soil surface salinity with high spatial resolution and the effect of heavy rains and spatial analysis of the resulting anomalies in Fars Province using satellite image processing.

Fars Province is located in the south of the central region of Iran with an area of 122.799 square kilometers. The topography of the province consists of mountains and plains. In this province, eight million ha of land are suitable for agriculture and gardens, although only 1.6 million hectares have been used. The agricultural sector in Fars Province, which accounts for a major share of the national gross product, plays one of the most important roles in Iran's production, employment, and food security, so many of the province's agricultural products, such as cereals and citrus fruits, rank first to third in the country. Since our study was carried out in a wide area of the country, it was decided to use Google Earth Engine (GEE) as an open-source platform. Also, the Generalized Difference Vegetation Index (GDVI) prepared by Wu (2014) was used to analyze soil salinity. In order to evaluate the efficiency of the obtained model, R2 and RMSE indices were used. In order to verify the output of the field data collected from the Agricultural and Natural Resources Research Center of Fars Province, which was used as a ground sample for the evaluation By using spatial analysis in the form of geostatistics, spatial structures can be identified and spatial planning can be done.

Results and Discussiom:

For the studied area, soil salinity was between 7.01 and 53.63 decisiemens/meter. The difference between the highest and the lowest soil salinity in the study area is approximately seven times, the highest value being in the east and south of the province in the cities of Niriz, Larestan, Lamard and Zarindasht. A significant point is the sharp increase in soil salinity in the bed of rivers leading to Bakhtegan and Tashk lakes. According to the available ground data, the accuracy of the map was checked, and the square root of the error and the correlation coefficient were calculated as 0.33 and 0.59, respectively. Then, the soil salinity map was extracted using the same algorithm in the period of Farudin 2018 due to the heavy rains that were associated with the arrival of numerous rain systems in Iran. Soil salinity was obtained between 6.35 and 47.9 and was classified into five classes. The results showed that changes have been made in the minimum and maximum values ​​of salinity and soil salinity levels and soil salinity has decreased especially in the south of the province.Then soil salinity anomaly was obtained and spatially analyzed. The term soil salinity anomaly means deviation from the reference value or long-term average. The results showed that the amount of abnormality increased and decreased between 0.8 and -0.9 in the province. Areas with lower salinity have experienced a greater share of positive anomalies. The positive anomaly was mostly around Darab, Zanian and Babamonir in the northeast of Jahrom. The southern and eastern parts, including Lar, Ozer, Rastaq, Ahl and Lamard, which were in the medium salinity class, have suffered less salinity anomalies. In order to understand the cluster or scatter pattern of soil salinity changes, Moran's spatial autocorrelation coefficient was investigated. The results showed that the anomaly of salinity distribution in the rainy year has a cluster pattern. By examining the available maps, it can be said that the clusters of soil salinity anomalies are mostly located in the north of the province, Baba Monir and Zarian at higher altitudes of the province and to some extent in the south of the province around Darab and Jahrom. Also, a little clustering has occurred in terms of anomalies in the plains of the province; That is, the rains could not cause major changes in the soil salinity of the plains.

In this research, the soil salinity map using GDVI in two time periods before and after the heavy rains of the water year 1398-1397, using the open source platform Google Earth Engine, extracting and changing the soil salinity classes and converting the salinity classes to each other, as well as the method of spatial clustering. The salinity anomaly was investigated. Soil salinity for the studied area was calculated between 7.01 and 53.63 decisiemens/meter with square error and correlation coefficient of 0.331 and 0.59, respectively. Soil salinity has changed between 6.35 and 47.9 after heavy rains. The most changes due to heavy rains are related to the low salinity layer with 19% and the least changes are related to the very saline layer with 0.3%. The amount of anomaly between 0.8 and 0.9 decisiemens per meter was increasing in the center of the province around Bakhtegan and Tashk lakes and the western highlands of the province and decreasing in the south and east of the province. Areas with lower salinity contribute more positive anomaly. The southern and eastern parts, which have high and very high salinity, undergo less changes. The results of this study showed that the use of remote sensing and satellite data in the Google Earth Engine cloud system and spatial analysis to prepare soil salinity maps in areas that have a large area and are affected by salinity changes, has great financial and time savings, and in Areas where sampling is not done or is associated with issues can be very efficient. Such researches can easily and quickly identify areas that are most exposed to increasing or decreasing soil salinity and can be used in environmental planning to implement preventive measures.

Sediment that moves with water is called suspended sediment, and the amount of suspended sediment material that passes through a river section in a certain period of time is called suspended load. The suspended sediment load (SSL) of a watershed, which passes through a certain section of the river, depends mainly on the climatic characteristics, the characteristics of the watershed and the capacity of carrying sedimentary materials. Actully Suspended sediment transport in the river is a function of meteorological and hydrological parameters as a complicated process. The input suspended load is one of the important and influencing factors on the amount of sediment input to reservoirs of dams and lakes. Determining the amount of sediment carried by rivers is important in many aspects. The calculation of suspended load is very important because of various reasons, one of the most important reasons is the role of suspended sediment load in the quantitative and qualitative management of surface water resources. Therefore, the distribution and transportation of suspended sediment load (SSL) in rivers have a significant effect on the water resource management, design of hydraulic structures, river morphology, water quality, and aquatic ecosystems. In fact, accurate and reliable modeling of suspended sediment load (SSL) is very important for planning, managing, and designing of river systems and water resource structures.  In addition, the determination of dry and wet periods is very important in studies related to water resources management, especially in arid and semi-arid regions.

To campare the result of the proposed models’ performance, the Cham Anjir, Bahram Jo, Kaka Reza and Sarab Syed Ali hydrometry stations in Khorramabad, Biranshahr and Alashtar sub-watersheds (a part of Kashkan watershed) in western of Iran, is used as a case study area. The geographic coordinates of the Cham Anjir, Bahram Jo, Kaka Reza and Sarab Syed Ali are 48° 15 '34" E 33° 26' 55" N, 48° 17' 45"E 33° 34' 8" N, 48° 13' 51" E 33° 43' 39" N and 48° 12' 14" E 33° 44' 55" N, respectively. The studied area has a semiarid climate with a mean annual rainfall Less than 500 mm. The studied area has a maximum elevation of 3578 m in Alashtar watershed and the minimum elevation of 1158 m in khorramAbad watershed. Most parts of the studied sub-watersheds are rangeland, while forest, dry farming, and irrigation lands are in considerable quantities. The surface lithology in the KhorramAbad, Alashtar and Biranshahr watersheds are covered by the Eocene, Quaternary, Cretaceous, Miocene, Oligocene, Paleocene, and Pliocene geologic formations.Predicting suspended sediment load (SSL) in water resource management requires efficient and reliable predicted models. The present study was carried out for the modeling of Suspended sediment load by learning algorithms in low and high discharge periods. In this study, five soft computing techniques, GP-PUK, GP-RBF, M5P, REEP Tree and RF were used to predict the SSL in Cham Anjir, Bahram Jo, Kaka Reza and Sarab Syed Ali hydrometry stations in Khorramabad, Biranshahr and Alashtar sub-watersheds. Total data set consists of rain, discharge and suspended sediment load (in a period of 18 years from 2000 to 2018). of three sub-watersheds out of which 70% data used to train the model and 30% data were used to test the model. Finally, the models’ accuracy was assessed using three performance evaluation parameters, which were Correlation Coefficient (C.C.), Root Mean Square Error (RMSE) and Maximum Absolute Error (MAE). Finally, a sensitivity investigation was executed to catch the best noteworthy input parameter during the modeling process. This process was carried out by eliminating the one input parameter and noted the output in terms of RMSE and C.C.

The obtained results suggest that the Gaussian Process (GP) model with two PUK and RBF kernels is more accurate to estimate the suspended sediment load (SSL) compared to the M5P, ReepTree and Random Forest (RF) models for the given study area. According to the results of the test part of the GP-PUK model, it has given us the best result, which are the correlation coefficient, the root mean square error and the mean absolute error in Bahram Jo station (0.55, 0.42, and 0.27), Cham Anjir station (0.74, 018, and 0.08), Sarab Seyed Ali station (0.71, 016, and 0.07) and Kakareza station (0.71, 0.24, and 0.15), respectively. In general, the Gaussian Process-PUK model, is the powerful model for the prediction of suspended sediment load (SSL) in low and high discharge periods. Therefore, according to the obtained results from this research, these optimal models can be used to costly and time-consuming tasks of the estimation of suspended sediment load from river. Also, these models can be used to estimate the suspended sediments load of nearby rivers by/without hydrometry station for the management of the quantity and quality of surface water. Also, sensitivity analysis suggests that  in Cham Anjir, Bahram Jo and Sarab Syed Ali hydrometry stations and rain in Kaka Reza hydrometry station, are the most significant parameters in estimation/prediction of SSL.

The present study focused on the development of a GP-PUK, GP-RBF, M5P, REEP Tree and Random forest (RF) models to estimate the suspended sediment load. For this purpose, the hydrometry and hydroclimatology data of the Bahram Jo, Cham Anjir, Sarab Syed Ali and Kaka Reza stations in Khorramabad, Alashtar and Biranshahr sub-watersheds composed of Suspended Sedimend Load (SSL), discharge, and rainfall data were used. In general, The major conclusions of the study are as follows: Among those models with the highest performance, the GP-PUK has the highest performance in both testing and training phases. -The GP-PUK predicted data are closer to observational data compared with the other model’s output data. Besides, the GP-PUK is the nearest predicted model with observational data.The GP-PUK model is one of the most extensively used data driven models in the erosion and sediment literature, while the usages of other data-driven models are comparatively lesser. Also, the structure of the GP-PUK is very simple and very less time consumable. Thus, the GP-PUK model can be useful in the Suspended Sediment Load (SSL) modeling not only foraccuracy but also for its time-saving nature and simple structure compared with other models.

Considering the large extent of the country's rangelands, studying their hydrology and soil erosion is important for choosing management scenarios. Rangeland exclosure is one of the watershed management methods that is used to improve vegetation and also control soil loss. Rangeland's exclosure of the Sanganeh Soil Conservation Research Site (SSCRS) started about 25 years ago, which has led to the improvement of vegetation in most of the slopes compared to the area under livestock grazing. However, vegetation has not been established on some slopes of the exclosure region. This research site has provided suitable conditions for soil erosion studies and assessment of rangeland management measures at the scale of the plot and small watersheds by having erosion plots of different lengths located on slopes with and without vegetation. In this regard, the present research is planned with the aim of determining the effect of rangelands exclosure 1) on soil loss in plots with and without vegetation, and 2) with different lengths.

In this research, a comparison of soil erosion was made in th e area under free livestock grazing (E6) and exclosure watershed with similar conditions (E4). In this regard, six erosion plots with lengths of 5, 10, and 15 m (with areas of 10, 20, and 30 m2, respectively) in two vegetation situations (with and without vegetation cover) were selected in each study watershed. After collecting the soil erosion data under 24 natural rainfalls, the effect of plot length and vegetation situation were compared by paired t-test.

The results of data analysis indicated that the maximum intensity of 30 minutes is in the range of 2.4 to 32 mm per hour with an average of 0.9 mm per hour. It can be clearly understood from the distribution of precipitation data based on the seasons of the year that the average rainfall of three seasons, spring, autumn and winter, is almost equal. However, the average rain erosion index (EI30) in spring is 3.02 and 4.40 times higher th an the corresponding values in autumn and winter, respectively. The reason for this is the occurrence of heavy rains in spring. The comparison of two fields with cover and without cover in the areas of exclosure and under livestock grazing in terms of soil erosion showed that in both areas (with cover and with out cover) the amount of soil erosion in the plots under grazing is significantly higher than in exclosure area. So, in similar rainfalls in the region, the soil erosion in the watershed under grazing at different slopes is from a minimum of 282% to a maximum of 550% more than the exclosure watershed. By increasing the length of the plot, soil erosion per unit area decreases. The decreasing trend of specific sediment with the increase in the length of the flow path is mainly due to the decrease in the amount of specific runoff and as a result, it is not possible to move the eroded materials in the plot. In other words, due to the dominance of the surface erosion process and the lack of development of rill erosion in the investigated plots, the re-infiltration of runoff and the deposition of transported materials are the most important reasons for the decreasing relationship between the increase in the length of the plot and erosion per unit area. In both slopes (with and without vegetation), the effect of waterlogging on soil erosion has increased with the increase of the area of the plot. In other words, grazing pasture in 10 m2 plots has reduced soil erosion by 287.7 and 324.6 percent, respectively, in slopes without and with vegetation. Meanwhile, in the plots of 30 m2, the reduction of soil erosion was observed by 472.4 and 613.7 percent. On the other hand, in the slops with vegetation, grazing has a greater effect on reducing soil erosion. In this context, it can be stated that surface erosion and sheet currents are the dominant phenomena of erosion and sediment transport in the studied slopes, therefore, if the roughness and permeability of the soil increases due to the operation of pasture exclosure, it can be expected that the runoff flow in longer routes (from the slope 10 m2 to 20 and 30 m2) to increase the amount of material deposition. In this case, exclosure in the domain with a longer length has a greater effect on reducing soil erosion.

SSCRS with erosion plots in different conditions (length, vegetation conditions and pasture management) is a suitable place for scientific research in the field of pasture hydrology. In this research, the effect of plot length and vegetation status on erosion was evaluated using soil erosion data measured in two areas of exclosure and under livestock grazing. The results of this research showed that in both ranges (with and without vegetation), the amount of soil erosion in the plots under grazing is at a significant level of 1% higher than in exclosure region. So, in similar rains in the region, soil erosion in the watershed under grazing in different lengths is at least 282% more than the flooded watershed. Also, the results indicated that soil erosion per unit area decreases with increasing plot length. The decreasing trend of specific sediment with the increase in the length of the flow path is mainly due to the decrease in the amount of specific runoff and as a result, it is not possible to move the eroded materials in the plot. Finally, the results showed that in both fields with and without vegetation, the effect of waterlogging on soil erosion increased with increasing plot length. On the other hand, in the range with vegetation (compared to the range without cover), grazing has a greater effect on reducing soil erosion.The results of the surveys conducted at the SSCRS confirm the sensitivity and fragility of the dry grassland ecosystem in this region on behalf of the country's dry grasslands. In such a way that after a long period of flooding in the region, some slopes still lack vegetation. Therefore, the destruction of vegetation in some areas, especially steep slopes and sensitive formations, may not be easily returned to the original state, and great care must be taken in their management. Because these domains have high erosion and runoff production. Due to the existence of recorded information related to the ideal situation of the region (exclosure region), it is possible to create different grazing systems and evaluate its effects. Finally, it is possible to compare the information obtained from exclosure region, managed grazing and open grazing.

Increasing the efficiency of water consumption and water management is necessary to meet the water needs of agricultural plants which need to consider the variables affecting water consumption, including water needs and the amount of evaporation and transpiration. In this regard, evaporation and transpiration are important indicators in the process of plant growth and their amount is considered equal to the water requirement of the plant. On the other hand, climate change can affect water demand by changing the expected patterns for the average weather condition in a long term in a specific region or for the entire global climate. In the present study, the phenological stages of seedless barberry tree were determined based on field observations at Ghaen synoptic meteorological station. The seedless barberry tree is one of the commercial cultivars in Iran. In this research, the effect of climate change on the water requirement of barberry cultivation has been evaluated based on RCP scenarios in the near and far future.

In the field part, in order to identify the occurrence time of the phenology stages and temperature thresholds, a series of visits and daily and weekly notes were made in the field in the growing season of the barberry tree. For this purpose, a private and fertile commercial orchard with suitable cultivated area of ​​seedless barberry trees was selected. The studied garden group with three hectares of cultivated area in Qain city was identified as one of the most fertile gardens in the region. This private garden is located in Qain city, at the position of 33 degrees and 43 minutes of north latitude and 59 degrees and 10 minutes of east longitude and a height of 1432 meters above sea level. In this study, the phenology stages of seedless barberry tree as one of the commercial cultivars of Iran were determined. The BBCH coding system was used to record the phenology stages (Enriquez‐Hidalgo et al., 2020). This scale has a 100-part table with codes from 0 to 99 and is designed for different phases (Feldmann and Rutikanga, 2021). It was used in the synoptic meteorological station of Qain city during one year from the beginning of germination to the end of the dormant period. In fact, the codes of the phenology stages were observed and recorded in the field.After determining the phenological stages of barren barberry trees, the water requirement of the selected tree species has been calculated. In the next step, to determine the water requirement, the reference evaporation and transpiration rate must be multiplied by the plant coefficient. For this purpose, the available data including hours of sunshine, average temperature during the growing season, average rainfall, minimum temperature, maximum temperature, evaporation, and transpiration obtained from the National Meteorological Organization for 18 valid meteorological stations from 1987 to 2017 on hourly and daily time scales were used to predict the climatic condition. Toward this, the climatic condition of the near future (2059-2030) and the far future (2089-2060) has been predicted considering pessimistic (RCP8.5), and optimistic (RCP4.5) scenarios.

The results showed that barberry needs six phenological stages to complete the growth period from early April to late November. Also, the amount of water requirement for barberry treesin the base period (1987-2017) on a daily basis in the eastern region under study is more than in the west and northwest of the region. The water requirement in the northwest and west parts is more than in the east of the region under study, which is the reason for the increase in the length of the barberry phenology stage in the region has been mentioned. The results of climate change analysis showed that the daily water requirement of barberry (2030-2059) based on the RCP8.5 model during the growing season varies between 4.5-5.8 mm per day and the total water requirement is 990-1260 mm. According to the RCP4.5 model, the daily water requirement of barberry varies between 5.6-5.8 mm per day and the total water requirement is 1290-990 mm. The daily water requirement of barberry according to the RCP4.5 model (2060-2089) varies between 4-5 mm per day and the total water requirement is 960-1150 mm. Also, the daily water requirement of barberry according to the RCP8.5 model varied between 4.5-8.2 mm per day. The total water requirement of the barberry tree is 950-1300 mm.

The present study was conducted with the aim of measuring the phenology stages of the seedless barberry tree and the water requirement of the barberry tree according to the conditions of climate change in the areas prone to its cultivation in Iran. The results showed that the barberry tree needs six phenology stages to complete its growth cycle. The growth period according to climatic conditions and topography lasts from early April to late November. The results of estimating the water requirement in the base period showed that the cities of Kerman, Yazd, Qain, Birjand, Zahedan and Torbat Heydarieh need the most water during the growth stage (1330-1240 mm per day) and the lowest water requirement of the barberry tree in the north It is in the west and west of the country, but in the future, the amount of water needed by the barberry tree in the northwest and west is more than the center and east of the study area, which is the reason for the earlier completion of the phenology stages in the center and east of the country, for this reason, these areas are among the unsuitable areas. It is considered cultivation. Since the annual rainfall changes from year to year; Therefore, the irrigation project cannot be planned only based on one year's information, so long-term records are needed to calculate the effective rainfall based on the probability of occurrence. Cultivation of barberry is very desirable in terms of irrigation for dry and semi-arid areas where farmers are facing water shortage. Considering that water is the main and essential requirement of any product; Therefore, it is essential to estimate the water requirement of each plant.

Groundwater salinization is a major environmental problem, especially in arid and semi-arid regions of the world. Different natural processes and anthropogenic activities can cause groundwater salinity. Factors such as rainfall, evaporation, groundwater pumping, agricultural and industrial activities, and artificial recharge of aquifers can affect the salinity of groundwater. One of the important factors that cause the salinity of freshwater aquifers is natural saline waters. Natural salinization of groundwater has occurred in many regions of the world. This phenomenon has been introduced as "dryland salinity". As a result of this phenomenon, salts accumulate in soil and water and affect human life and natural ecosystems. Several factors cause the natural salinity of groundwater in semi-arid regions. These factors include locally derived cyclic salts, salts in wind deposits, salts in marine deposits, salt domes, unsaturated zone salts and, salts resulting from rock weathering. Delineating the origin and mechanism of salinity is an important help in preventing the degradation of groundwater quality and optimal management of groundwater resources. In recent years, the phenomenon of salinization of groundwater has been observed in some areas of the Faryab plain in southeast Iran. This study aims to identify the origin and mechanism of groundwater salinity in Faryab plain aquifer.

This paper presents an integrated geophysical and hydrochemical investigation of the saline water intrusion into the Faryab plain. Geophysical studies were conducted by the geoelectrical method and include 55 electric soundings. The results of geoelectric studies have been analyzed using iso-resistivity maps and geoelectrical profiles. Twenty-seven water samples were also collected from abstraction wells to assess the quality of groundwater. These samples have been analyzed to determine the concentration of main cations and anions. The results obtained from the chemical analysis of water samples have also been examined and analyzed using spatial distribution maps of qualitative parameters, bivariate diagrams, and time series of water salinity. The mixing rate of saline and fresh groundwater has also been evaluated using ionic ratios of Na/Cl and Cl/HCO3.

The lowest electrical resistance was recorded in the central part of the plain. The specific resistance of the saturated zone decreases towards the center of the plain. Therefore, fresh water and saline water are hydraulically connected. According to the spatial variations of specific resistance, the Faryab plain is divided into two regions: one area with a resistance of more than 50 ohm-m and one region with a resistance of less than 10 ohm-m. The area with low specific resistance (less than 10 ohm-m) is observed in the center of the plain. The most important reasons for the existence of this area are the high groundwater level, the surface saline layers, and the salinity of groundwater. The electrical conductivity of groundwater reaches 64000 μmohs/cm in the center of the Faryab plain. The highest amount of sulfate and chloride ions are also observed in the water samples of this area. According to the Gibbs diagrams, the groundwater has been also influenced by the evaporation process.

In this study, the origin and mechanism of groundwater salinity in the Faryab plain in southeast Iran were investigated. Fine-grained sediments have been deposited in the central part of this plain. In the past, the underground water level in this area was a little far from the ground level and caused an evaporation zone of surface and underground water. As a result of water evaporation in the central part, evaporative sediments including chalk and salt sediments were formed and the amount of undergroundwater salts increased.Based on the results of geoelectrical studies, in the central areas of the plain, the amount of specific apparent resistance decreases, which indicates the presence of fine-grained sediments containing salt water in these areas. Investigating the quality characteristics of underground water also shows the occurrence of processes such as dissolution of halite, dissolution of gypsum and the occurrence of cation exchange process in the aquifer. Evaporation from underground water has also affected the water quality, especially in the middle part of the plain. As a result of high pumping from the production wells, the hydraulic load of the aquifer has decreased and the saline groundwater zone has expanded towards the production wells. Saltwater intrusion has caused salinity and quality degradation of underground water. According to the results of this research, the most important reason for the salinity of underground water in Faryab plain is excessive extraction of underground water and disturbing the natural balance of the aquifer. The most important solution to deal with the development of the salt water front in the Faryab plain is to reduce the exploitation of the aquifer, especially in its middle areas. It is also suggested to modify and improve existing artificial feeding facilities and locate new artificial feeding projects to control saltwater intrusion. Sampling from different depths of the aquifer and measuring minor ions such as iodine and bromine will greatly help to better understand the groundwater salinization process. Measurement of environmental isotopes such as oxygen-18, deuterium and chloride-36 will also help to enrich future studies. By preparing the mathematical model of the aquifer, the effect of different management measures on the salinity control of the aquifer can be investigated. By using the results of the mathematical model, the aquifer balance can be checked and the amount of underground water withdrawal can be determined.

Water resources are strongly influenced by the hydrological cycle and the estimation of evapotranspiration as the main component of the hydrological cycle plays an important role in water resources management. This phenomenon is nonlinear and very difficult to estimate in the sense that there are many parameters involved in its estimation. There are many methods to estimate evapotranspiration none of which is free from problems. Some of these methods, such as Lysimeter, are costly and time-consuming, and others, such as empirical methods only have local value. Therefore, the application of a method which is able to model this phenomenon based on its entity and with minimum data seems necessary. In recent years, the use of artificial intelligence models to simulate various problems has become very popular. In terms of performance, artificial neural networks are very efficient models whose computational speed is completely independent of the mathematical complexity of the algorithms or the method used. The purpose of this research is to compare artificial neural network models, neural network model optimized with genetic algorithm and experimental models in estimation of reference evaporation and transpiration using meteorological data in Ramhormoz synoptic station.

As mentioned above, the aim of this study was to compare the models of artificial neural network (ANN), artificial neural network optimized with genetic algorithm (ANN + GA) and experimental models (Hargreaves-Samani, Blaney-Criddle and Irmak) in estimating reference evapotranspiration compared to the results obtained from the Penman-Monteith FAO standard model by using Meteorological data in Ramhormoz synoptic station. For this purpose, meteorological parameters of Ramhormoz synoptic station were collected monthly during the years 2011 to 2018. This information includes: minimum temperature, maximum temperature, average temperature, wind speed at 2 meters, minimum relative humidity, maximum relative humidity and was sunny hours.Artificial neural networks are simplified models of the working system of the human brain, which are not comparable to natural systems. These models try to imitate human thought processes.The process of using artificial neural network models includes three stages of training, verification and testing. In the present study, 70% of the data was considered for training, 10% for validation and 20% for testing. Also, the stimulus function considered for the training and test phase is the sigmoid tangent.To extract better results from the artificial neural network model, it is necessary to optimize the parameters used. To determine the most optimal parameters required for the artificial neural network model, such as the number of layers, neurons and the weight of the layers, a lot of time is spent on their calibration using the trial and error method. For this reason, in this research, the combination of artificial neural network model and genetic algorithm (ANN+GA) was used in order to achieve the optimal parameters of the artificial neural network model. Minimizing the amount of simulation error as a function of the objective function and the number of iterations was considered as the stopping condition of the optimization algorithm.

Overall, the results showed that artificial neural network models to empirical models used to model higher correlation with the Penman-Monteith FAO model. In addition, among the neural network models used, the integrated neural network model with the genetic algorithm has a higher correlation with the Penman-Monteith FAO model. So that the value of R2 in Blaney Kridel, Hargreaves Samani, Airmak, ANN and ANN+GA models is 0.65, 0.819, 0.781, 0.969 and 0.973, respectively. The results of using scenarios using meteorological parameters as input for ANN and ANN + GA models showed that the highest accuracy of estimating reference evapotranspiration in both models is related to the scenario with input data such as temperature. The minimum is the maximum temperature, wind speed at a height of 2 meters, minimum relative humidity, maximum relative humidity and sunny hours, and the lowest accuracy of the model was in a scenario with two inputs of maximum temperature and minimum temperature. Among the experimental models, Hargreaves-Samani, Irmak and Blaney-Criddle models had the highest correlation with the standard Penman-Monteith FAO model, respectively.

Evapotranspiration is one of the important factors in the hydrological cycle and among the determining factors of energy equations on the earth's surface and water balance. In this regard, many researchers tried to estimate the amount of evaporation and transpiration with a suitable approximation using a cheap and easier method for different regions. The purpose of this research is to compare artificial neural network (ANN) models, artificial neural network optimized with genetic algorithm (ANN+GA) and experimental models (Blaney-Criddle, Hargreaves Samani and Irmak) in estimating reference evaporation and transpiration compared to the obtained results. It was done from the standard Penman-Monteith-FAO model, using meteorological data at Ramhormoz synoptic station. The general results of this research showed that the artificial neural network models have a higher correlation with the Penman-Manteith-Fau model than the used experimental models. In addition, among the used neural network models, the integrated neural network model with genetic algorithm has a higher correlation with the Penman-Manteith-Fau model than the artificial neural network model. Also, among the experimental models used, respectively, Hargreaves Samani, Irmak, and Blaney-Criddle models have the highest correlation with the standard Penman-Monteith-Fau method. In line with the results of the present research, it is suggested to compare the results of experimental models and artificial neural network with the data obtained from the evaporation pan.

Prediction of hydrological variables, especially precipitation, is very important in the management and planning of water resources. For this reason, accurate estimation methods have always been of interest to researchers. Furthermore, due to the water crisis in different regions, it is necessary to use different methods to predict the rainfall and the resulting runoff so that comprehensive and appropriate management can be applied in the field of water distribution. Since the past, various methods have been developed and used by researchers to predict hydrological variables. The use of classical methods such as multiple linear regression to predict hydrological variables, especially precipitation, has been one of the most important and widely used methods that have had good results. Recently, data mining methods have been developed for this purpose. In this research, a comparison between the performance of the classic multiple linear regression and modern data mining methods was made in the annual rainfall modeling of Ahvaz city, and finally the best model in terms of performance was determined.

In this study, the annual rainfall of Ahvaz city has been investigated and modeled. Meteorological data from Ahvaz station was collected over a period of 30 years (1992-2021). The data validation tests including tests of homogeneity, normality, trend, and outlier data were performed. Annual rainfall modeling of Ahvaz city was done with Multiple Linear Regression (MLR), Principal Component Analysis (PCA), Gene Expression Programming (GEP), and Support Vector Machine (SVM). Finally, using the coefficient of determination (R2), Root Mean Square of Errors (RMSE), Nash-Sutcliffe Efficiency (NSE), and Willmott index (WI), the accuracy and performance of the models were compared.

In this study, XLSTAT software was used to model rainfall with multiple linear regression. In order to simulate precipitation through the SVM model, it is possible to examine the types of kernel function, among which linear and polynomial kernels of the second and third degree, which are common types used in hydrology, are selected and through trial and error the optimal results of this The type of kernels was calculated. According to these results, the support vector machine model with third degree polynomial kernel was determined as the optimal method of precipitation modeling. In simulating the precipitation process using gene expression programming, because this model has the ability to select more effective variables and eliminate variables with less influence, therefore, in this project, all eight input factors are used to determine meaningful variables and for further investigation, in addition to the set The default mathematical operators of the program (F1), modes based on the values of the four main operators (F2) and the set of operators F3 and F4 have been used.The results of the validation tests that check the homogeneity, trend, normality, and outlier data showed the good quality of the recorded data and the possibility of using them with a high percentage of confidence to continue the study. The results of comparing the models showed that the methods of PCA and GEP with R2=0.85, NSE=0.85, and WI=0.96 and very little difference in RMSE equal 35.49 and 35.70, respectively. They have predicted the annual rainfall of Ahvaz with better performance and more accuracy compared to other models. Considering the water crisis in different regions of the country, especially in Ahvaz, it is suggested to use the methods introduced in this research to predict rainfall and runoff resulting from it, so that a comprehensive and appropriate management can be applied in the field of water distribution.

In this research, a comparison was made between classical statistical methods and some modern data mining methods in forecasting the annual rainfall of Ahvaz city. The hydrological data of Ahvaz synoptic meteorological station was collected in a period of 30 years (1371-1400) and first the data was verified using homogeneity, trend, normality and outlier data tests. The results showed the good quality of the recorded data and the possibility of using them with a high percentage of confidence. Multiple linear regression (MLR), principal component analysis (PCA), gene expression programming (GEP) and support vector machine (SVM) methods were used to model precipitation. The results of running the models were compared using the coefficient of explanation (R2), root mean square errors (RMSE), Nash-Sutcliffe efficiency (NSE) and Wilmot index (WI). The results showed that the methods of principal component analysis and gene expression programming with R2 criteria equal to 0.85, NSE equal to 0.85 and WI equal to 0.96 and a very small difference in RMSE values equal to 35.49 and 35.70, respectively, compared to Other models have better performance and more accuracy.According to the results of this research, it is suggested to use modern data mining methods in addition to classical statistical methods in future researches. Also, it is necessary to pay attention to the use of functions and optimal factors of models to achieve the best results in future researches. Considering the water crisis in different parts of the country, especially in Ahvaz, it is suggested to use the methods introduced in this research to predict the rainfall and runoff caused by it, so that a comprehensive and appropriate management can be applied in the field of water distribution.

Land use/cover information is vital to the dynamic monitoring, planning and management, and the reasonable development of land. Recently, due to human activity, land cover information has changed dramatically. Furthermore, construction, land has become increasingly scarce, and the non-agriculturalization of arable land has been highlighted. Therefore, it has become increasingly significant to timely, and accurately monitor land use and land cover for the reasonable development and utilization of urban land resources in city regions. It is significant to timely, accurate, and effectively monitor land cover for conservation, reasonable development and land resources. The remotely sensed dynamic monitoring of covered land in rapidly developing regions has increasingly depended on remote-sensing data on temporal and spatial resolutions. In many cases, it is difficult to acquire enough time-series images with high quality at both high temporal and spatial resolution from the same sensor.In this research, Landsat images and an object-oriented method were used to eliminate errors using the visual interpretation method to prepare a land cover map and achieve acceptable accuracy and classification results. Landsat 8 data was used to prepare the land cover map using spatio-temporal integration model. In addition, through an object-oriented classification method, land cover was extracted, which was used to provide a more accurate and efficient technical method for effectively extracting land cover information in Bushehr province.

In this paper, we proposed a method for mapping land use and land cover in a Bushehr province area with high spatial-temporal resolution using fusing Landsat 8 time series. The method has three steps, 1) Enhance the spatial-temporal adaptive reflectance fusion model (ESTARFM), 2) Determination the optimal data combined for the extraction of cover type, 3) Image segmentation and Land cover extraction and the accuracy assessment based on the field sample method was used. In many cases, it is difficult to acquire enough time-series images with high quality at both high temporal and spatial resolution from the same sensor. This study used the temporal-spatial fusion model ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) to blend Landsat 8 and MODIS data and obtain Landsat 8 images. Then, the cover lands information of Bushehr province is extracted using an object-based classification method.

In this paper, an object-oriented LULC mapping method using time series Landsat 8 images was proposed. Based on the time-series Landsat 8 data (red band, NIR band and NDVI), the land cover types. In this study, the proposed method was used in a case study of Bushehr province. The results of the object-based method show that the overall accuracy and Kappa coefficients were 93.34% and 0.86, respectively, and the user/producer accuracies of cover lands in the pixel-based method were all over 80%. The approach presented an accurate and efficient technical method for effectively extracting land use information in heterogeneous regions. In this paper, we have achieved an acceptable classification result using Landsat 8 image. There are still some potential factors affecting the accuracy. The first factor is the uncertainties of the fused images by the ESTARFM fusion model. The second is the mixed pixel problem, the Landsat image pixels are always informed with several land cover types, which will affect the classification accuracy. Remote sensing images with high spatial resolution may be a feasible way to achieve higher accuracy.

In the present study, based on the combined data of Landsat 8 and object-oriented classification, the land cover map of Bushehr province was extracted. Types of pasture and desert land cover were clearly presented. In this research, an object-oriented method was presented for the preparation of pasture cover type map using the images generated from ESTARFM time-spatial integration model. Based on Landsat 8 data (red band, near-infrared band and normalized vegetation difference index), the types of pasture cover in Bushehr province were extracted using visual and object-oriented methods.The use of Landsat 8 images and the object-oriented method improved the classification. But there are some factors that affect the accuracy of preparing land use and land cover maps. The first uncertainty factor is the fusion images by the enhanced spatio-temporal adaptive reflectance model (ESTARFM). The second problem is the existence of heterogeneous pixels. In heterogeneous areas, the presence of small polygons in which the pixels of the Landsat image of vegetation cannot be separated in these polygons, which affects the classification accuracy. Remote sensing images with high spatial resolution may be a practical way to achieve higher accuracy. Therefore, the object-oriented method combined with spectral integration analysis can be improved to achieve land cover information extraction. The results of this research showed that the overall accuracy and Kappa coefficients in the object-oriented method were 93.34% and 0.86, respectively, and the accuracy of the land cover user/producer in the pixel-oriented method was more than 80%. The object-oriented algorithm analyzes images as objects by merging neighborhood information, which enhances the analysis and increases the classification accuracy. The object-oriented algorithm has shown its potential in identifying land cover and preparing land cover in heterogeneous areas.

Soil is one of the most important components of the ecosystem and environment for storing nutrients and performing biological, physical, and chemical processes and activities. Earth may be infected by the accumulation of heavy elements. These metals are ubiquitous, highly persistent, and non-biodegradable. The concentration of heavy metals increases due to the natural weathering of rocks, the disposal of waste, and the use of fertilizers, pesticides, and industrial effluent. It includes nickel, which is released by car brake abrasion, vehicle corrosion (especially the car oil pump), and electronic wastes in the urban environment and its accumulation in the body causes kidney complications, lung damage, high blood pressure, and it became vascular diseases. Nickel has no toxic effect on the plant at low concentrations and acts as a micronutrient, but in high concentrations, it reduces the growth and appearance of toxic symptoms in the plants. In agricultural fields, chemical fertilizers are used to increase the production of agricultural products, which despite their benefits, their excessive use reduces crop quality and the entry of toxic pollutants into the soil. In this context, organcic amendments such as biochar and vermicompost could be useful to sustainably maintain or increase soil organic matter, preserving and improving soil fertility and crop yield. Biochar is a carbon-rich material obtained from the thermochemical conversion of biomass in an oxygen-limited environment. Biochar has been described as a possible tool for soil fertility improvement, potential toxic element adsorption, and climate change mitigation. Vermicompost is considered as a high-nutrientbiofertilizer with diverse microbial communities. It plays a major role in improving the growth and yield of different field crops, vegetables, flowers, and fruit crops. Vermicomposting is the process of conversion of organic wastes into finely degraded peat-like substances using earthworms. It is an alternative method for waste management through which vermicompost is produced with a relatively high nutrient content than compost and manures. Therefore, this study aimed to investigate the effect of biochar and vermicompost on an accumulation of nickel metal in soil and tomato fruit.

Materials and Methods :

For this purpose, the seeds were caught from a greenhouse in the south of Tehran, and the pots were filled with 3 kg of soil derived from the same place. This soil was mixed well before being placed in the pots. The test was performed as a factorial experiment in a randomized complete block design in which nickel nitrate was applied to the soil with 6 different concentrations (75 ppm nickel nitrate with 10% biochar, 150 ppm nickel nitrate with 10% biochar, 75 ppm nickel nitrate with 5% biochar, 150 ppm nickel nitrate with 5% biochar, 75 ppm nickel nitrate and 150 ppm nickel nitrate), and compared with pots that were only hydrated with tap water (control). All measurements were taken with three independent replicates for metal concentration. Pots were placed outdoors with tap water irrigation (five times a week) and two days a week (Monday and Friday) with nickel nitrate for 4 months. Then the concentration of nickel in the shoot (fruit), the nickel concentration in the soil, and the wet and dry weight of the fruit were measured. The bioconcentration factor (BCF) was also calculated using the ratio of total nickel concentration in fruit to the soil. Based on this, plants that have a bioconcentration factor of more than once, especially in their shoots, are suitable for metal extracting from the soil and translocating it to the fruit.

In general conclusion, it can be stated that planting cherry tomatoes in nickel containing soil or irrigation with municipal and industrial wastewater containing nickel should be accompanied by more considerations. Also, if biochar or vermicompost is used in the soil of cherry tomato pots, nickel can be extracted from the soil with a bioconcentration coefficient

Groundwater, one of the most important sources of water supply, has faced a considerable decrease in quantity and quality in recent decades due to harvesting more than their natural recharge. Over-extraction of groundwater causes subsidence, soil salinity, reduction of the base flow of rivers due to the drying up of springs, the salinity of groundwater, and in a general view, land destruction and the creation and expansion of desert areas. Desertification is one factor that threaten human life, which causes the destruction of natural resources. Therefore, knowledge of desertification processes is important and necessary to reduce the severity of this phenomenon and prevent its spread by providing suitable management solutions and methods. The Iranian Model of Desertification Potential Assessment (IMDPA) is efficient to evaluate the intensity of desertification. In this model, nine key criteria including climate, land-geomorphology, soil, vegetation, economic-social, agricultural water, groundwater, erosion, and urban development technology are considered. According to the reports about the spread of desertification factors in the Maharloo-Bakhtegan watershed located in Fars province, the objective of the present study is to investigate the impact of the water table drop and variations in the groundwater quality and land subsidence in desertification and land degradation as well as to prepare relevant maps in the mentioned watershed.

Maharloo-Bakhtegan watershed is one of the closed watersheds of Iran, which is a subcategory of the Central Plateau and has 27 study areas. A zoning map of the risk of water table drop was prepared using the related tables that classify the watershed area into four classes: low, medium, severe and very severe. In the same way, the data of quality degradation indicators of water resources including electrical conductivity (EC) and sodium surface absorption ratio (SAR) were classified; then, EC and SAR zoning layers were prepared. Then, the final desertification intensity map was obtained from the integration of maps of the intensity of qualitative and water table drop of groundwater resources, as well as the land subsidence map as a soil criterion. Finally, the zoning map of desertification intensity was prepared using the final risk classes of desertification.

The EC map showed that 37.2% of the watershed is in the low or insignificant class, 24.8% is in the medium class, 9.5% is in a severe class, and 28.5% of the watershed area is in the very severe class. So, it can have a significant effect on increasing the intensity of desertification. However, the zoning map of the SAR showed that more than 99% of the watershed area has a value less than 18 and is placed in the negligible or low class. The zoning of the water table drop also shows that 65.6% of the studied watershed is placed at the low or insignificant class, only 3% is in the medium class, 5.7% is in a severe class, and 25.7% is in the very severe class. The map of different levels of land subsidence showed that more than 97% of the watershed area either has no subsidence or is placed in a low and negligible level. The desertification intensity map also showed that 83% of the watershed area has low and moderate desertification intensity and 17% of the watershed area has severe and very severe desertification intensity. Therefore, the general situation of the watershed is not critical in terms of the intensity of desertification, but the areas with a moderate degree of destruction are subject to transition to extreme class due to the change of land uses that are expanding today as well as the increase of agricultural areas in the watershed.

The present study was conducted with the aim of investigating the effect of the decrease and change in the quality of underground water and land subsidence in desertification and land degradation in Maharlu-Bakhtegan watershed. The results showed that the electrical conductivity factor of underground water can have a significant effect on the intensity of desertification. On the other hand, land subsidence index was not prioritized in determining the intensity of desertification. Also, the intensity of desertification in a major part of the watershed was in the low and medium classes, and with the change of current uses and the increase of the cultivated area, it is recommended to make the necessary plans to prevent the progress of the intensity of desertification in the mentioned areas. In this direction and considering the role of human activities in the drop of the underground water level, the quality drop of the underground water and finally the subsidence of the land, measures such as the preparation of technical guidelines for the use of underground water, the use of pressurized irrigation systems, the proper implementation of the balancing plan of the Ministry of Energy and the implementation Management plans are recommended to preserve the vegetation of forests and pastures and to modify the cultivation pattern. Also, the zoning maps of indicators and intensity of desertification prepared in this study can be used by experts to prepare the land and provide suggested solutions and apply appropriate management methods with the aim of preventing the expansion and advancement of saline and desert lands. Conducting research in the field of indicators and other factors affecting desertification, such as evaporative formations in the region, the gentle slope of the water table in the plain, the presence of semi-permeable or impermeable layers, the lack of underground water flow from upstream and the infiltration of salty water from salty rivers into the aquifers of the river in the watershed. Maharlo-Bakhtagan is also recommended.

Almond (Prunus dulcis Mill.) is one of the oldest and most critical dry fruits in the world and belongs to the Rosacea family. Salinity is one of the increasing problems in the world and it covers a large part of our country, so identifying work methods against abiotic stress is a major challenge. Salinity stress affects the absorption of nutrients. In the conditions of salinity stress, the occurrence of changes in the amount of absorption, distribution and transfer of nutrients in the plant parts or the physiological inactivation of the parts of the plant that are involved in the absorption of nutrients cause disturbance in the nutritional balance of the plant. Salinity first leads to a decrease in water absorption and then causes a disturbance in the absorption of nutrients in the plant, which leads to plant damage. Absorption of water and nutrients in plants are closely related, and the factors that limit water absorption may also cause tension in the absorption of nutrients. In the conditions of salinity stress, the decrease in the absorption of nutrients is caused by the decrease in the efficiency of the roots in the absorption of nutrients and the competition between sodium and chlorine ions with elements such as calcium and potassium. The use of almond-specific hybrids such as GF677, GF557, Titan, Hansen, and GN15 is beneficial for achieving salinity and drought tolerance. However, botanists need faster and more complete methods to cope with intense environmental stresses. Using effective microorganisms (EMs) is one of these options that increases plant resistance to environmental stresses through increasing metabolism. Therefore, this study was conducted to select new salinity-resistant rootstocks and investigate the effect of different levels of salinity and EM' treatments on the number of various nutrients in the leaf and root of almond seedlings.

This research was carried out in the Department of Horticulture, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili in 2016. A pot experiment was carried out as a factorial based on the randomized complete block design (RCBD) with three replications. Almond rootstocks, two years old, healthy, and with the same growth ability, were provided. In March, they were removed from polyethylene pots and transformed into seven kg plastic pots containing equal amounts of cultivated soil and peat moss and each pot was considered as one repeat. The used soil texture was sandy loamy with pH = 6.8, EC = 1.63 ds m−1, 179 ppm of K, 71 ppm of P, 0.17 % of total N, and 1.41 % of C. The experimental treatments included: effective microorganisms concentrations containing EM0: zero, EM1: 10, EM2: 20, and EM3: 30 ml L-1 and salinity in four levels: B0: zero, B1: 60, B2: 120, and B3: 180 mM NaCl, that were applied on almond rootstocks: C0: Sangi almond seedling, C1: GF677, and C2: GN15. From the 5th of May and for two months, EMs have been applied once a week with irrigation water and soil application. The pots were irrigated with water containing various NaCl levels every two days, from the 5th of July for 60 days. Drainage water was removed from the bottom of the pot at each irrigation with saline water. The plant roots were thoroughly washed with ordinary water to minimize EC and pH changes due to salt accumulation in the planting bed each week. At the beginning of the experiment, to prevent the occurrence of sudden stress on plants, salinity stress increased by increasing the amount of 15 mM NaCl daily. To evaluate these conditions, on the studied almond rootstocks, nutrient elements such as sodium, chlorine, potassium, calcium, and nitrogen, and the potassium/sodium ratio of leaf and root were measured.

Analysis of the variance (ANOVA) showed that in the treatment of EMs, the interaction of the year and the rootstocks was significant on calcium leaf. The simple effects of treatments in all studied elements were statistically significant. The results showed that the sodium chloride of leaves and chlorine and roots increased with increasing salinity stress. Three tested rootstocks with stress expansion, but the GF677 and seedling rootstock had the lowest sodium and chloride in the leaf and root. With increasing salt stress levels, concentrations of potassium, calcium, nitrogen, and potassium/sodium ratio of leaves and roots, decreased in all three rootstocks but these indices were higher in the GF677 and seedling almond.

Among the evaluated traits elements, the leaf nitrogen, and potassium/sodium content were good markers to study and examine the salinity of almond rootstocks. The application of biofertilizers improved all studied indices in each of the three studied rootstocks and the best result in the Ems experiment was related to the GF677, the level of 30 ml per liter of EMs, and zero mM of salinity. Based on this, the GF677 hybrid was tolerant to salt stress conditions, and seedling almond and GN15 were placed in the next positions, respectively.

Livestock grazing has a direct or indirect effect on the density, diversity, and distribution of vegetation cover in Rangelands. Excessive livestock grazing is one of the destructive pressures on rangelands, which causes a decrease in diversity, and loss of sensitive plant elements. In this regard, maintaining species diversity and abundance is one of the goals of managing natural ecosystems. The concept of species diversity is a combination of two interconnected components of richness and uniformity. Species richness means the number of species per unit area and uniformity means the distribution of individuals and plant species per unit area. Many species diversity indices have been invented so that species diversity can be studied using them. Considering the importance and role of both components of species diversity, ecologists want to evaluate and study indicators that examine both components and determine the contribution of each. In this regard, several methods have been proposed for the evaluation and studies of plant diversity. Two major groups of these methods include numerical indices (richness, uniformity, and diversity) and parametric indices (for example, frequency distribution models, category-frequency diagrams, dominance-diversity, and diversity grading curves). In this research, the condition of vegetation cover, frequency, abundance, and diversity of species in three rangelands with light, medium, and heavy grazing intensities in the rangelands of Sarbisheh City is investigated.
 
This research was carried out in the plain rangelands of west of Sarbisheh City located in South Khorasan Province. First, after the field visit and observing the effects of destruction in the soil and vegetation caused by the intensity of livestock grazing and the presence of invasive plants in the studied rangelands, In the whole field of research, The effects of light, medium and heavy grazing (each in three separate plant types) were observed. Sampling in each plant type was done randomly and systematically. The percentage of plant cover and density was evaluated by visual and counting methods, species abundance based on the Ranquier method, species diversity using numerical indices, and species abundance using statistical and ecological models. Kruskal-Wallis non-parametric test was used to compare vegetation characteristics and species diversity in three intensities of livestock grazing. In order to select the best model for fitting the Four species data with statistical models, akaike's information criterion (AIC) was used.
 
The result of the Kruskal-Wallis non-parametric test showed that the intensity of livestock grazing had no significant effect on any of the studied characteristics. Nevertheless, the rangeland under light grazing had a higher coverage percentage and plant density (42.27 % and 28,000 plants per ha) than the rangeland under medium and heavy grazing. According to the Shannon-Wiener index, very low diversity was assessed in two rangeland with light and medium grazing intensity (H=1.78 and H=1.69, respectively), and low diversity was assessed in the rangeland with heavy grazing intensity (H=2.02). Rangeland with moderate grazing intensity is relatively lower in species diversity (H=1.69 and 1-D=0.66) and higher in richness (15 species) and species dominance (D=0.34). Based on the AIC criterion, the results showed that rangelands with light, medium, and heavy grazing intensity were best fitted with the logarithmic series, lognormal series, and logarithmic series models, respectively. In communities where the sampling volume is large, she recommended three logarithmic, Shannon, and Simpson indices, which have low to moderate sensitivity to plot size and are widely used.
 
The results of the present research showed that species abundance patterns change due to the intensity of livestock grazing. By comparing the results of species abundance models in the current research with other research, it can be said that in areas with high species richness, with increasing grazing pressure (from low to high), the distribution of species abundance changes from the normal log series (representing a stable society) to a geometric or log-normal series (representing an unstable society) But in the present research, because the rangeland has a poor condition in terms of species richness and diversity, species abundance distribution models are not a suitable indicator for distinguishing the three intensities of livestock grazing.

Optimal cultivation patterns are necessary for the sustainable development of agriculture and the protection of land and water resources, and the need for it has deepened. In the context of sustainable agricultural development, the optimization of the cultivation pattern, in addition to the economic benefits, should also follow the subsequent environmental effects. Due to the limitation of water resources in different regions of the world, joint use of surface and groundwater has become very important. Optimizing the joint use of surface and groundwater has become a necessary contribution to sustainable irrigation methods, and due to this, there is a need to improve methods of joint use of surface and underground water. The purpose of this research is to use a new comprehensive structure of multi-objective simulator-optimizer in order to simultaneously formulate the optimal cultivation pattern and the optimal allocation of surface and groundwater resources for the integrated management of the water resources system and solving complex water resources problems.  

The study area in this research is Shahriar Plain in Tehran Province, Iran. Shahriar Plain is located on the western outskirts of Tehran city. In this study, a multi-objective simulator-optimizer modeling pattern was prepared and for this purpose, it was first simulated using the groundwater modeling system (GMS) of the groundwater level. Then six scenarios including a 50 % increase in artificial recharge, 50 % increase in artificial recharge and 30 % reduction in consumption from exploitation wells, 50 % increase in artificial recharge and 40 % reduction in consumption from exploitation wells, 30 % reduction in consumption from exploitation wells, 40 % The reduction of consumption from exploitation wells, elimination of drinking and industrial exploitation wells and its impact on the aquifer was defined based on the groundwater level in order to optimally exploit the aquifer of the study area. After this step, the groundwater level was estimated using the artificial neural network (ANN) model. Finally, the two objective functions of income to cost and groundwater level changes were estimated based on the constraints related to the conditions of the desired area by a multi-objective genetic algorithm (NSGA-II).

The GMS model in a steady state was calibrated. The RMSE error was 0.71 meters and the maximum and minimum differences between observed and calculated values were calculated as 1.73 and 0.001 meters, respectively. The maximum amount of yield water in the calibration stage of the unstable state is 0.0976 and related to the northern regions of the area and due to the coarseness of the alluvial formations in these areas, and its minimum value is 0.0003 and related to the southern regions of the area. The results obtained from the model showed the RMSE error at this stage and the verification mode equal to 0.72 and 0.98 meters, respectively. Also, the budget resulting from the third scenario of the GMS was estimated to be 203 (MCM), which has increased by 313 (MCM) compared to the budget resulting from this model in the water year 95, and has caused an increase of the groundwater level by 13 meters. Therefore, the model has adequately simulated the groundwater flow in the aquifer. Also, the results of the optimization model showed the highest amount of optimal irrigation demand for Eslamshahr district at 66%, then Shahriyar at 20%, and finally Robat Karim at 14%. Optimal water demand volume and area under cultivation in the total state, have decreased by 36%, and the volume of groundwater consumption by 74 percent compared to the current conditions. The amount of optimal water consumption (surface water and groundwater) of agricultural products also shows the values of 36, 39, and 25% respectively in Shahriar, Eslamshahr, and Robat Karim districts, which according to this issue, water consumption in the agricultural sector is in optimal conditions compared to the current situation has decreased by 44 %. The highest parameter of the ratio of income to cost obtained is related to Shahriar district, then Robat Karim district and finally Eslamshahr district. The results of the simulator models show the groundwater level changes resulting from the third scenario compared to the neural network model by 11 m. Finally, Optimal cultivation pattern planning and exploitation of water resources compared to the third scenario of the model and the neural network model, was chosen as the pattern of optimal water planning.   

Products such as vegetables, alfalfa, onions, grapes, pears, and pomegranates can be used more in the study areas, because the amount of income to cost and volume of water consumed have been suitable, and these factors have been very effective in increasing the net profit and changes in the groundwater level, and prevent the occurrence of crime and water complex problems. Also, optimal cultivation pattern planning and exploitation of water resources compared to the third scenario of the GMS and the neural network model, was evaluated as the selected optimal planning pattern of water resources. Therefore, by applying the desired research policies and optimal management and control of the cultivation pattern of agricultural products and available water resources, in addition to preventing the occurrence of crisis in water issues, environmental and economic problems can also be reduced.

The rainfall decrease and limited water resources in arid and semi-arid regions such as the Zahedan region caused the groundwater resources to need more attention in these areas. Moreover, the increase in the population in Zahedan Country and the lack of a suitable system for collecting wastewater in this area have made the pollution of underground water inevitable. So that, the excessive extraction of underground water resources and successive droughts on the other hand have caused a decrease in the underground water level in this region. Therefore, the present study was carried out with the aim of time-spatial monitoring of underground water quality in Zahedan County.

The aim of this study was to monitor the temporal-spatial quality of groundwater quality in Zahedan City in four stages. Determining the geographical coordinates of sampling wells, calculating the water quality index (WQI), and Wilcox index, comparing the interpolation methods in ArcGIS 10.8 software to prepare the spatial map of the WQI and comparing the spatial variations of the WQI. This research was conducted in two time periods 2010-2013 and 2014-2017. To investigate the temporal changes in water quality parameters, annual statistics of 90 wells and aqueducts with continuous statistics were used. Then, WQI and Wilcox’s indices were calculated for drinking and agricultural uses, respectively. One of the most practical methods is WQI, which indicates the degree of its suitability for various uses, including drinking. This index is usually obtained from the values ​​of general water parameters including dissolved oxygen, acidity, hardness, soluble solids, temperature, turbidity, nitrate, nitrite, and some basic ions. This index combines different water quality parameters to provide a numerical value that can be used for spatial comparisons. Besides, Wilcox's classification is one of the most important index in the field of determining the quality of agricultural water, which is calculated based on two parameters of electrical conductivity (EC) and sodium absorption ratio (SAR) as the risk of alkalinity.

The lowest and highest WQI levels were 30.1 and 674.0, respectively in the period 2010-2017. WQI time changes during the study period showed that the WQI is not significantly different between the first (2010-2013) and the second (2014-2017) periods. According to this index, in the first period, 7.9 % of the studied wells were in good condition, 5.6 % in bad condition, 13.5 % in very bad condition, and 73 % undrinkable in terms of drinking water quality. In the second period, 6.7 % of the studied wells were in good condition, 6.3 % in bad condition, 13.3 % in very bad condition, and 73.3 % undrinkable in terms of drinking water quality. In both time periods, the quality of none of the wells in terms of drinking was not in the high class. The results of descriptive statistics showed that the mean parameters of SO42-, Cl-, EC, TH, TDS, and HCO3- were two intervals higher than the standard. According to the results of the Pearson correlation test, there is a negative significant relationship between the pH and other parameters. The pH has the most negative significant relationship with TH. TDS and EC variables have the highest positive and significant relationship. This lack of impact can be attributed to the range of very low pH changes in groundwater samples in the two periods. There is also a significant negative relationship between pH and other parameters. The pH has the most negative and significant relationship with potassium. Moreover, according to the Wilcox index, in the period of 2010-2013, 87 % of the studied stations are in the S4C2, S3C3, S3C4, and S4C4 classes, which are unsuitable for agriculture and 12 % were in the C3S3 class, which by applying the necessary measures can be used for agriculture. Likewise, in the period 2014-2017, 91 % of the sampled stations are located in very salty and unsuitable for agriculture, and 8 % of wells. It is possible that the high extraction of underground water and the drop in the water level in the Zahedan region have caused the EC of water to increase. Also, drought can play a role as an aggravating factor in the change in water quality. The results of the Pearson correlation test showed that there is a significant negative relationship between pH and other parameters. Since in the comparison of various interpolation methods based on RMS and R2 for the WQI, the zoning map was drawn based on the kriging method due to its lowest RMS and highest R2.

Due to the high salinity of water, the study wells are not used for drinking and agriculture purposes but they can be used in ordinary applications such as washing. Besides, considering the quality characteristics of groundwater in the Zahedan region, it can be acknowledged that proper water management in this region is very important.

In recent years, we have witnessed the unsustainable use of water resources, which has led to short-term and long-term water crises. The diversity of water and soil resources, along with climate change, has made the scientific management of agricultural water inevitable. In a such scenario, managing scarce water resources to meet ever-increasing needs is challenging. To make the best use of water resources, it is important to know the amount of water needed for economic production.Determining the water requirement of crops, especially the evapotranspiration potential, indirect ways and in different climates for agricultural and orchard plants is one of the basic strategies of each region. Evapotranspiration (ET) is a process that includes two parts: evaporation (evaporation of water from the surface of soil and vegetation and surface water) and transpiration (evaporation of water from plant organs due to plant physiological activities). The purpose of estimating evapotranspiration is to determine the crop’s water requirement, and irrigation planning, and to evaluate the sensitivity of crops’ performance to water deficiency in different stages of plant growth. Sugar beet is one of agricultural crops that is placed in the cultivation pattern and is cultivated in a wide area of the world due to the need for sugar consumption. Determining the evapotranspiration of this crop and planning its irrigation is of particular importance. Numerous studies showed that the water requirement of sugar beet, based on the variety and climate, differs. Various techniques have been proposed to measure ETc, and each method has advantages and limitations. Some of the widely used methods are lysimetric experiments, eddy covariance, Bowen ratio, energy balance method, and soil water balance method. In recent years, new technologies are also used to estimate evapotranspiration, among them, the Surface Energy Balance Algorithms for Land (SEBAL) can be mentioned. This research aims to compare ET, water requirement, and water productivity of sugar beet in lysimetric data to SEBAL algorithm using Landsat 5 satellite images, from 1996 to 1998.

This experiment was carried out at the Chahar-Takhteh research station (Shahrekord, Iran) at latitude 50 ̊56ʹ and longitude 31̊ 11ʹ, 2066 m above sea level.In the spring of 1996, 1997, and 1998, before planting the crop, the soil inside the lysimeter was irrigated to reach the saturation level. Two days after irrigation and at field capacity, monogram seeds of sugar beet, at the rate of 120,000 crops per hectare, were cropped. The row spacing in the field around the lysimeter was similar. Irrigation was based on the discharge of about 35 to 45 % of the moisture content at field capacity. The required amount of water was calculated by the neutron probe and added to the lysimeter. simultaneously, the surrounding area was also irrigated.Remote sensing data included Landsat 5 satellite images for the years of experiment, path 164, and row 38. The temporal resolution of the satellite was 16 days. Spatial resolution for visible, near, and mid-infrared bands was 30 and 120 m for thermal infrared. The 25 cloud-free images were downloaded (6, 9, and 10 images) for research years. These images were retrieved from the website (https://earthexplorer.usgs.gov) as geometrically and radiometrically corrected and processed in ERDAS Imagine 2022 software. To estimate actual evapotranspiration, the energy balance equation is used, λET=Rn-G0-H. In this equation, Rn is the net incoming radiation flux, H is the sensible heat flux, G0 is the soil heat flux, and λET is the latent heat flux of evaporation (W/m2 ). The statistical indicators include mean absolute error (MAE) which is unsigned, mean bias error (MBE), root mean square error (RMSE), normalized root mean square error (NRMSE), and Coefficient of Determination (R2 ).

Evapotranspiration of sugar beet in lysimeter and in SEBAL on the days of satellite passage in 1996 to 1998 (25 overpasses without clouds) showed that the difference of evapotranspiration in the two methods was -1.20 % and -0.13 mm d -1 which showed high accuracy. The negative sign means that the SEBAL estimates were lower than the corresponding values in the lysimeter. The statistical indices values of RMSE, NRMSE, MAE, and MBE for 25 pairs of evapotranspiration values were 0.7031 mm d -1 , 0.1102, 0.5552, and -0.1312, respectively. The RMSE, NRMSE, MAE and MBE statistical indices for 18 pairs of monthly evapotranspiration were 54.1155 mm month-1 , 0.3225, 40.9462, and - 28.7955, respectively. The total values of evapotranspiration in lysimeter were equal to 1096.6, 1022.6, and 906.3 mm during the growth period (total mean equal to 1040.6 mm) from 1996 to 1998, respectively. The total values of evapotranspiration in the SEBAL were equal to 1004.6, 831.6, and 666.4 mm during the growth period, total mean of 834.2 mm. The mean difference was around 19.8%. The results of mean water productivity were 5.02 kg m-3 in lysimeter and 6.26 kg m-3 in SEBAL. Because of lower evapotranspiration values in SEBAL compared to the lysimeter, the water productivity values were higher.

Determining the water requirement of crops is the basis of planning for the sustainable use of water resources and irrigation of crops. The sugar beet has a great amount of evapotranspiration due to its large green cover. Accurate quantification of crop evapotranspiration (ETc) at local and regional scales can help water policy and decision-making in water resources and their management. The results indicated that the SEBAL algorithm using Landsat 5 satellite images with a coefficient of determination (R2=0.9889) in the daily time period and a coefficient of determination (R2=0.9318) in the monthly time period had a good correlation with lysimetric data. In general, results showed that SEBAL has a special capability as one of the widely used remote sensing algorithms to estimate crop evapotranspiration.

In recent years, the changes in the intensity and frequency of precipitation and the occurrence of severe floods and droughts have prompted decision-makers to consider the effects of climate change in their plans. Due to the existence of impervious areas in urban environments, a more significant part of precipitation is convertedto runoff, and the changes in precipitation patterns resulting from climate change can affect the performance of drainage systems. On the other hand, with the change in precipitation pattern, the amount of pollutants washed from the surface is changed, and in this way, the quality of runoff is also affected. Nowadays, coupled atmosphere-ocean general circulation models (AOGCMs) are considered the most advanced and reliable tools for simulating climate change. Recently, a coupled model intercomparison project Phase 6 (CMIP6) hasbeen introduced as the latest version of AOGCMs, which can simulate future periods with high accuracy. The sixth assessment report evaluates the changes in climate variables by combining Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP) scenarios. According to this report, in addition to covering different climates, future scenarios should also consider the socio-economic aspects of development. The CMIP6 models have higher spatial resolution than the models of previous reports. A review of the research background in the assessment of climate change effects on precipitation and runoff shows that most of the studies have been conducted using the models and scenarios of the fifth and earlier reports, and only a few of them have included the scenarios of the sixth report in their evaluations. In this regard, our research evaluated climate change effects on urban runoff based on the CMIP6 models’ predictions.

District 10 of Tehran municipality is selected as the case study. This region is located in the south of Tehran and has an area of about 800 ha. Due to its high population density, lack of enough green space, and a high percentage of impervious areas, runoff management is a priority for this region. Moreover, the presence of agricultural land highlights the need for runoff quality management in the study area. The MehrAbad synoptic station is the nearest to the case study, and its observation data during the base period is gathered for evaluating the changes in hydrological variables under climate change. For implementing the methodology, different CMIP6 models were first assessed, and those with high performance in precipitation prediction in the historical period were selected. Their projections under SSP1-2.6 and SSP5-8.5 for the future (2021-2050) were downscaled using the LARS-WG model. Then, the statistical method was employed for disaggregating the LARS-WG’s daily output into 6-hour design precipitation. In the following, maximum and minimum values of precipitation were determined as optimistic and pessimistic scenarios, respectively, and the stormwater management model (SWMM) was implemented for simulating the runoff under these scenarios. The SWMM subdivides the watershed into sub-watersheds and utilizes three primary processes for runoff quality and quantity simulation. First, generated runoff is calculated by determining the hydrological characteristics of the sub-watersheds in the hydrologic process. Then, the canals route runoff to the outlet using a hydraulic process. In the quality process, the runoff quality is simulated using build-up and wash-off equations. In our research, the study area was subdivided into 84 sub-watersheds. Changes in runoff volume, peak flow, and total suspended solid (TSS) concentration at the watershed outlet were evaluated under climate change. Correspondingly, for assessing the performance of the drainage system, the changes in the flooded volume of the system were quantified.

According to the results, in all models under SSP5-8.5, monthly precipitation will increase in January, February, and March and decrease in August and September. Also, under SSP1-2.6, the precipitation trend is predicted to fall in September. The highest increase in precipitation compared to the base period is related to August under SSP1-2.6. In addition, with a decrease of 37.4 %, the highest reduction in precipitation is associated with February. The most evaluated prediction uncertainty is related to August under SSP1-2.6. This month, precipitation changes range from +226.31 to -18.34 % compared to the base period. Also, predictions on an annual scale do not show a specific trend. The changes in annual precipitation vary from a -9.8 % decrease to a 5.4 % increase compared to the base period. Then, by analyzing the 6-hour rain, the predicted values of HADGEM3-GC31-LL and CMCC-ESM2 were identified as the highest and lowest values, respectively. The 6 h rain with 5 and 10-year return periods under the pessimist scenario will increase by 31.4 and 26.8 % and decrease by 2.5 and 11.3 % under the optimistic scenario, respectively. The results of performing SWMM under a pessimistic scenario showed that in the return periods of 5 and 10 years, runoff volume would increase by 25.2 and 20.7 %, and TSS concentration will decrease by 21.4 and 18.2 %, respectively. Besides, in this scenario, the flooded volume of the basin increases to 42.12 %. Performing SWMM under an optimistic scenario revealed that with the reduction of precipitation compared to the base period, in the return period of 5 and 10-years, the runoff volume will decrease by 2.2 and 8.3 %, and the TSS concentration will increase by 2.5 and 10 %, respectively.

Performing SWMM under an optimistic scenario shows that with the decrease of 6-hour design precipitation, the quantitative parameters (runoff volume and peak flow) decrease, and TSS concentration increases at the watershed outlet. Furthermore, under the pessimistic scenario, quantitative parameters increase, and TSS concentration decreases with the increase in precipitation. More examination revealed that despite the decline in precipitation, the number of flooded nodes remained constant under optimistic scenarios indicating the drainage system’s vulnerability even under base-case rain and a little less. Moreover, the increase in flooded volume and the number of flooded nodes under the pessimistic scenario make it necessary to utilize management strategies to improve the runoff collection systems’ performance under climate change. In this regard, low-impact development (LID) practices can be used as a climate change adaptive approach in future works.

Crusts are a hard layer on surface soil, that is formed by disaggregation– aggregation process in which particles of soil, air, water, and organic matter are connected to each other and classified into different types including physical, chemical, and biological. Chemical crusts like salt crusts are formed due to intense evaporation on the surface of extremely salty soils. Physical crusts are formed raining or through irrigation of agricultural lands and are divided into three categories including structural, erosional, and sedimentary, depending on the process of their formation. The biological crust which is formed by the function of algae, cyanobacteria, mosses, and lichens, and due to their positive protective roles and restoration ability received much attention so far. However, studies on the effects of non-biological crust and their protective role have been considered less. Various effective factors on crust strength have been investigated but land use has been left out. This research has focused on modeling land use effection crust strength, in dust emission sources in the southeast of Ahvaz.

In the south-eastern of Ahvaz in Khouzestan province, three land uses including agriculture, agroforestry, and barren land were selected. In order to measure the strength of the surface crust in selected land uses, a handheld penetrometer was used and crust strength was measured in random points in 30 points in each land use. To reduce the influence of other environmental conditions, measurements were done scattered on each land use. Then, to obtain the strength in one point, three measured points were averaged, and finally, 90 measured points were obtained for each. The surface soil moisture of land uses was done by taking soil samples and measuring in the laboratory, and then significant differences between land use groups were tested by analysis of variance. Normality and homogeneity of variances were tested by using the Kolmogrove-Sminov and Levene's tests on soil strength data set. Due to the fact that the soil texture is different in studied land uses and also the soil texture is one of the most important factors affecting the strength of crust in the measured points, the soil texture was extracted from the existing maps. In order to investigate the effect of these two independent factors on the crust strength as well as their interaction, General Linear Modeling (GLM) was chosen to exclude the soil texture effect on crust strength variation and model the land use effects.

Results showed that soil surface moisture does not have a significant difference in land use groups. By using the General linear model, crust strength was modeled. In the first stage, the effect of land use and soil texture were investigated as the most important factors affecting the hardness of the crust and the results showed that land use and soil texture as well as their interactions are effective in changing the hardness of the crust at the level of 95 % and 99 %, respectively. These factors have an effect on the variance of crust hardness, but the main source of variance is land use, and this factor alone explains about 78 % of the crust strength variance, and the model explains 96 % of the variance of the dependent variable and the presented model is significant at the level of 99 %. In order to check the existence of a significant difference in crust strength in studied land uses, Helmert's Contrast and Bonferroni tests were used. The result showed that there is a significant difference in the average crust strength of barren lands with agriculture and agroforestry at the 99 % level, and no significant difference is observed between agriculture and agroforestry. Then, in order to investigate the single factor of land use, the soil texture was considered as covariance, and its effect on the hardness of the crust was removed. The results showed that there is a significant difference in the average hardness of barren land use with agriculture and agroforestry at the level of 99 %. The presented model explains 86 % of the variance of the hardness of the ridge, and among the factors with a significant level, 99 % of the hardness of the crust in a barren land with 70 % partial effect has the largest role in explaining the variance. With the change of land use from agroforestry to barren land, the hardness of the soil surface increases by 50 %, and with changing to agricultural land, it decreases by 14 %.

In agricultural and forestry land uses, with the increase in the traffic of people and heavy machinery, the crust is broken and does not return to its original strength. Based on these results, it can be said that in desert areas, vegetation conservation is not the only way to protect soil from wind erosion, but protecting the crust against traffic and breakage can be an efficient solution that has received less attention. Legal confrontation with land use change and land plowing can be a sustainable solution for these areas. It is suggested that with a general assessment of the surface strength of the crust on bare land, easily can be protected against the wind only with management practices.

Remote sensed information on growth, vigor, and dynamics from terrestrial vegetation can provide useful insights for applications in environmental monitoring, biodiversity conservation, agriculture, forestry, urban green infrastructures, and other related fields. Specifically, these types of information applied to agriculture provide not only an objective basis (depending on resolution) for the macro- and micro-management of agricultural production but also on many occasions the necessary information for yield estimation of crops. Vegetation indices (VIs) obtained from the vegetation canopy in remote sensing are simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications. These indicators are used in remote sensing applications and satellite systems. To date, there is no unified mathematical expression that defines all VIs due to the complexity of different combinations of light spectra, instrumentation, platforms, and resolutions used. Therefore, special algorithms have been developed for different applications according to the specific mathematical expressions in the range of the visible light radiation spectrum, mainly the green spectrum region, from vegetation, and invisible spectra to quantitatively determine the level of vegetation cover. In this article, the spectral characteristics of vegetation and vegetation indices, the advantages and disadvantages of various developed indices, and their application are discussed according to the characteristics of vegetation, environment, and accuracy of implementation.

Remote sensing of vegetation is primarily performed by getting the electromagnetic wave reflectance data from canopies utilizing passive sensors. It is well known that the reflectance of light spectra from plants changes with plant sort, water substance inside tissues, and other natural components. The reflectance from vegetation to the electromagnetic range (spectral reflectance or emanation characteristics of vegetation) is decided by chemical and morphological characteristics of the surface of organs or clears out. Most applications for inaccessible detecting of vegetation are based on the taking after light spectra: (i) the bright locale (UV), which goes from 10 fr 380 nm; (ii) the apparent spectra, which are composed of the blue (450–495 nm), green (495−570 nm), and ruddy (620–750 nm) wavelength districts; and (iii) the close and mid-infrared band (850–1700 nm). The emissivity rate of the surface of takes off (equivalent to the absorptivity within the warm waveband) of a completely developed green arrange.

Many studies have constrained this translation by extricating vegetation data utilizing person light spectra groups or a bunch of single groups for information investigation. Hence, analysts regularly combine the information from near-infrared (0.7–1.1 m) and ruddy (0.6–0.7 m) groups in numerous ways concurring with their particular targets. These sorts of combinations display many disadvantages (e.g., need for affectability) by employing a single or restricted gathering of groups to detect, for case, vegetation biomass. These impediments are especially apparent when attempting to apply these sorts of VI on heterogeneous canopies, such as green tree ranches. A blended combination of soils, weeds, and cover crops within the interrow. The plants of intrigued make the segregation locales of intrigued and extraction of straightforward VI exceptionally troublesome, particularly, when the vegetation of intrigued has distinctive VIs due to spatial inconstancy, or VIs compared to other vegetation (weeds and cover edit), which can be compared to those of intrigued. The last mentioned will complicate imaging denoising and sifting forms. A few picture examination procedures and calculations have been created to go around these issues, which can be depicted afterward. Indeed in spite of the fact that there are numerous contemplations as portrayed sometime recently, the development of a straightforward VI calculation seems numerous times to render basic and compelling apparatuses to degree vegetation status on the surface of the soil. Vegetation data from remotely detected pictures is primarily translated by contrasts and changes within the green clears out from plants and canopy ghastly characteristics. The foremost common approval preparation is through coordinate or backhanded relationships between VIs gotten and the vegetation characteristics of intrigued measured in situ, such as vegetation cover, leaf area index (LAI), biomass, development, and vigor evaluation. More set-up strategies are utilized to evaluate VIs utilizing coordinate and georeferenced strategies by checking sentinel plants to be compared with VIs gotten from the same plants for calibration purposes.

Basic vegetation records combining obvious and near-infrared groups have essentially moved forward the affectability of green vegetation discovery. Diverse situations have their variable and complex characteristics that must be considered when utilizing distinctive plant lists. Hence, each vegetation list has its claim definition of green vegetation, its reasonableness for particular applications, and a few restricting variables. Subsequently, for commonsense applications, the choice of a particular vegetation file ought to be done carefully by considering and comprehensively analyzing the points of interest and confinements of existing vegetation records and after that combining them for application in a particular environment. In this way, the utilization of plant markers can be custom-fitted to particular applications, instruments, and stages. With the advancement of hyperspectral and multispectral further detecting innovation, it is conceivable to create unused plant markers that grow investigative areas.