نشریه پژوهش های حفاظت آب و خاک
سال سی‌ام شماره 2 (خرداد و تیر 1402)

Natural vegetation is a vital component of ecosystem change models and conservation measures. The current study investigates the spatio-temporal dynamics of Normalized Differential Vegetation Index (NDVI) and Improved Vegetation Index (EVI) with atmospheric indices such as precipitation (P) and Multivariate Standardized Precipitation Index (MSPI); biological index such as evapotranspiration (ET) and soil moisture (SM); and soil index, land surface temperature parameter (LST) with the help of satellite images in Google Earth Engine system during 2000-2021 in Hormozgan province.

Google Earth Engine system images were used to evaluate plant indices with climatic and biological variables. NDVI and EVI indices, evaporation and transpiration and earth surface temperature from 16-day Terra satellite images and soil surface moisture (0-10 cm) from Open Land Map, during the period from 2000-2021 were extracted monthly by programming functions for the region. Rainfall and MSPI drought index were prepared with the help of 22-year statistics of the stations. A precipitation map was prepared using the Kriging geostatistics method in ArcGIS software. Using the PCA method, the drought index was calculated in different places with SPSS software. In order to better understand the changes in time, their anomaly plant indices were extracted over two decades. Correlation and change trend of parameters were calculated by Spearman and Mann-Kendall method.

Spearman's correlation coefficient and Man-Kendall's trending method between NDVI and EVI indices are significant at the 99% level of 0.84 and 0.67, respectively. The time distribution of plant indices showed that their annual and monthly changes are consistent with the amount of autumn cultivation. According to the hot and dry climate of the province, most of the rains are from January to March. The indices had the highest values during the months of January, March and April. This period corresponds to the season of planting and harvesting agricultural products, and the plants have reached their highest growth stage. Results showed the spatial changes of the plant indices correspond to the high rainfall areas and the edges of rivers and agricultural and garden lands. The appropriate overlap of precipitation zones and MSPI drought index with the spatial distribution of plant indices, ground surface temperature, and soil moisture indicates the determining role of precipitation in arid and semi-arid regions. The anomaly results showed that there is a weak positive correlation during the period of 2011-2000 due to frequent and severe droughts. In the second decade of 2011-2021, due to the decrease in the severity of the drought, the indicators showed a significantly strong correlation. According to the MSPI drought index, the western, central and eastern regions with the highest amounts of drought and lack of rainfall have the lowest amount of plant indices.

The expansion of the area has caused more concentration of vegetation indicators in some areas. The highest values of NDVI and EVI are in the area along the banks of rivers and water sources. These areas correspond to agricultural centers in the plains of Rodan, Minab, Shamil and Takht and parts of Haji Abad, which are geographically in the north-eastern regions and parts of the north. Vegetation dynamic trends and their relationship with other factors should be studied in the long term to establish a pattern. This study has been done as much as possible and according to the availability of a data set. Long-term studies can be done by extracting experimental variables at the regional level and in order to influence the population growth on the region. The findings of this study can help future studies related to vegetation and its relationship with other factors, nature protection and resource allocation.

In Iran, the climatic conditions are such that even in the rainiest areas of the country, there is a need for groundwater resources, and this demand is increasing every year. Since, groundwater is one of the most valuable water resources in Iran, it is very necessary to predict its changes in order to use it optimally with the aim of sustainable development. One of the most complex hydrological processes in nature is the rainfall-groundwater level process, which is affected by various physical and hydrological parameters. Although, various models have been presented to predict the changes in the groundwater level using the rainfall patterns, but less attention has been paid to the transfer function model. Hence, the main objective of this research is to introduce and use the transfer function (TF) model to predict the monthly groundwater level using rainfall data and to compare its results with ANFIS and Artificial Neural Network (ANN) models.In the present study, 30-year data (1992-2021) of meteorological stations and observation wells in 3 watersheds of Galikesh, Ramian and Mohamadabad were used to model the rainfall and groundwater level. of the Gorganroud river basin.Then, considering that the years closer to the present time have more accurate information about the situation of this time, the years were considered as a forward process in artificial neural networks. The model fitting and prediction of the groundwater level values using rainfall data for the next 12 months was performed with applying three models: Artificial Neural Network (ANN), Adaptive neuro fuzzy inference system (ANFIS), and transfer function (TF). For this purpose, MINITAB SAS, SPSS, and R software were used. Next step, the validation of the values predicted by the models was evaluated using three indices Mean Absolute Distance (MAD), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).The results of the autocorrelation plots of the groundwater level of the wells revealed that all time series have a seasonal trend with a period of 12 months. Based on the cross-correlation plots, it was also found that rainfall has direct effect on the groundwater level in the two watersheds of Galikesh and Mohamadabad with lag time of three months and in the Ramian watershed with a delay of one month. The validation results of the models using three indexes MAD, RMSE and MAPE revealed that the artificial neural network model for predicting the groundwater level using monthly rainfall data in all three investigated watersheds had the most appropriate performance (RMSE=0.0778, 0.0243, 0.0532m) and the ANFIS model is ranked second (RMSE=0.1841, 0.0832, 0.1012m). Although the transfer function model was less accurate than the other two methods (RMSE=0.5711, 0.5023, 0.3234m), but this model has performed well in fitting the monthly groundwater level values. This model is very effective in identifying the delay in the impact between the input and output variables, as well as expressing the model based on which the impact of rainfall can be expressed as a model.The results of this research show that all three models of artificial neural network, ANFIS and transfer function can be used to predict the groundwater level using monthly rainfall values. Consecutive overestimation and underestimation, which increases the error and decreases the performance of the models, was not observed for the three used models. Also, all three models perform well in detecting trends and data changes. However, the artificial neural network model is more accurate than the other models. In addition, when forward process is used in artificial neural network modeling, compared to the case where the complete series of data is used, the efficiency of the model is significantly improved.

Floods are one of the most damaging natural disasters in the world. So better understanding of the flood hazard phenomenon and its potential consequences is necessary. The present research is a part of the flood risk management approach based on the KULTURisk framework, in which vulnerability is examined as one of the flood risk components. The aim of the research is to assess the vulnerability of local communities exposed to the flood occurrence in the current conditions, and also to predict the effects of intervention scenarios to reduce it. Flood vulnerability assessment requires quantifying vulnerability components. In this research, the components, variables and indicators for the region existing conditions were selected based on the intended aim, region field observations and related literature review. The required data and information were collected using questionnaires and interviews with the local communities, statistical yearbooks, population and housing census data center, instructions and guidelines for the management system of statistics and hospital information, as well as referring to related executive organizations at the city and province levels. The statistical population includes local communities living in the 100-year flooding areas in a part of the Qarachai River in Ramian city (one of the tributaries of the Gorganroud River in the Golestan province). The reason for choosing this reach is the location of the villages in the vicinity of the river and the frequency of flood events that have occurred in the past years. The index values were normalized by classification scale and distance to reference methods and the weight of each index was determined by the Delphi method. The normalized index values were converted into spatial layers in the GIS environment. Then, a vulnerability map of the target area was prepared by combining the spatial layers of susceptibility, adaptation capacities and coping capacities variables. Based on the results, three intervention scenarios including early warning system establishment, risk governance intervention and risk spreading were selected in order to reduce the level of vulnerability in the face flooding for the study area. The degree of vulnerability of flood-affected areas in the study area for the current conditions was placed in the "highly vulnerable" class, while by applying intervention scenario under combined scenarios (including early warning system establishment, risk governance intervention and risk spreading), the vulnerability degree would change to "slightly vulnerable" level. The results of this research, is helpful for better understanding of the phenomenon of flooding and its potential consequences by the people and authorities of the Seyed Kalateh village. It also will facilitate the prioritization of measures appropriate to the region conditions in order to reduce vulnerability by increasing the capacity of adaptation and tolerance. Since, the effectiveness of the proposed measures such as creating an early warning system, intervention risk governance and risk spreading on reducing vulnerability and consequently reducing the amount of human and financial losses has been recognized, it is an important step in the direction of flood risk management at different scales. Moreover, the analysis shows the necessity of vulnerability assessment and adoption of specific policies at the local scale (the Seyed Kalateh village), while this issue is usually not considered in technical and financial planning of crisis management especially for flood risk management.

Prediction of land use changes in explaining the interactions between ecosystems and human activities is important for helping decision makers. A Land use map is considered an information resource in natural resource management. Optimal resource management needs to be investigated, as recognition of changes and resource degradation in the past, and proper and principled planning in order to control and control possible future degradation. The purpose of this study was to evaluate the performance of the Markov chain model (CA Markov) in determining and predicting land use changes for the future in the Ghaleh Jogh area located in the city of Torbat-e-Heydarieh.

Satellite imagery, remote sensing, and the Markov chain model were used for modeling and detecting land use change. The study area of the Ghaleh Jogh Watershed is from Torbat-e-Heydarieh province. A topographic map with a scale of 1: 25,000 and OLI, ETM +, and Landsat MSS images of 1987, 2002, and 2015 were used to prepare land use maps and the changes process. Satellite imagery with first order polynomial equation was corrected by the method of closest neighboring geometric correction and corrected using linear and histogram explanations. For the categorization of images, the controlled classification method and the maximum probability algorithm with acceptable accuracy are used. A Land use map was prepared in 4 ranges of pasture land, Bayer lands, crops, and gardens. In this research, using the CA Markov model, the 2015 forecast map was compared with the monitored map. The percentage of land use, garden, rangeland, lawn, and arable land use varies from 5, 54, 24, and 17 percent in 1987 to 7, 4, 7, 36, 51, and 7.6 percent in 2015, and the user's map Lands for 2025 and 2040 are foreseen. Model calibration was used for predicting the 2015 model.

The results showed that according to the Kappa coefficient, the prepared map has high accuracy. Based on the results of the detection and simulation of the pasture and cultivating lands, they are among the most unstable classes. Garden lands due to more attention and economic significance, there have not been any significant changes in their extent. In general, the largest changes occurred in pasture and agricultural land. The corrective work done in the region has reduced the area of arable land and added to the rangelands.

In arid and semi-arid areas, due to the lack of sufficient vegetation and the return of a small amount of plant residues to the soil, the amount of organic matter in the soil is very low, so the use of organic fertilizers can be a suitable solution to improve the physical and chemical conditions of the soil in these areas. In recent years, biochar has been used as a soil modifier (source of organic carbon) and as a method for carbon sequestration in the soils of dry and desert areas. this research aims to maintain and increase the water retention capacity in the soil and improve the physical characteristics of the loam sandy of the Sistan region by using different compositions and sizes.

In this research, the effect of three biochar diameter ranges (<0.25, 0.25-1 and 1-2 mm) and three amounts of Cenocarpus biochar (2, 4, and 8% by weight) on the properties Hydraulic and physical soil and evaporation from the soil surface with loam sandy texture of Sistan region were studied. Experiments were conducted in 3 replications in a completely randomized design. To carry out the work, polyethylene columns with a diameter of 10 cm and a height of 35 cm were prepared. The columns were covered with cloth from the floor. The soil with different treatments of size and amount of biochar was manually added to the columns and filled with gentle blows to the column body until reaching a height of 30 cm. To measure evaporation, 100 mm of water was added to the soil in 5 weeks (20 mm per week). Evaporation from the soil surface was measured by weight method every day. Hydraulic conductivity of saturated soil was measured by constant load method. Also, soil salinity and moisture were calculated at three depths of 0-10, 10-20 and 20-30 cm. The bulk density and water holding capacity of the soil were also measured.

The results showed that with the increase in the amount of biochar used, the bulk density and saturated hydraulic conductivity of the soil decreased by 29.32% and 48.1%, respectively, compared to the control treatment (treatment without biochar). By increasing the amount of biochar and decreasing its particle size, the water holding capacity in the soil increased. The treatment with 8% by weight of biochar and the particle size <0.25 mm had the greatest (43.54%) increase in the water holding capacity in the soil. By increasing the amount of biochar and decreasing its particle size, soil salinity increased compared to the control treatment. But the evaporation from the soil surface decreased with the increase in the amount of biochar and the decrease in its particle size.

According to the obtained results, it can be said that biochar has positive effects on improving the physical and hydraulic properties of soils with a relatively light texture. But these changes depend on the amount and size of biochar particles. Using biochar as a soil modifier in light textured soils that have low water holding capacity and high evaporation, especially in arid and semi-arid areas that are facing the problem of lack of water resources, is a very suitable solution. But it should be noted that increasing biochar levels in soil can increase soil salinity. Therefore, according to the obtained results and the positive effect of biochar on the physical and chemical parameters of the soil, the optimal selection of the biochar surface should be such that its use is economically viable, and this requires more research, especially It is in farm conditions.

One of the main factors in the economic and social development of any country is water reserves and potential. Comprehensive and comprehensive knowledge of water resources is a prerequisite for optimal and sustainable use of these resources. In Iran, due to the lack of rainfall in most of the watersheds and the limited water resources, codified planning to know the possibilities and limitations of water resources with the aim of optimal utilization is very necessary and unavoidable. Today one of the basic strategies for managing water resources in the agricultural sector is to choose the appropriate cultivation pattern and determine the strategies for the optimal allocation of agricultural water. the optimal use of limited water resources and obtaining the greatest economic benefit.

This research was conducted on the cropping pattern of Gonbad-e Kavus region in Golestan province, using the irrigation network of the Golestan Dam for four major autumn crops (wheat, canola, barley, and triticale) in the agricultural years of 2017-2018 to 2020-2021. A multi-objective optimization was performed using the non-dominate sorting genetic algorithm (NSGA-II) with the objectives of maximizing net economic profit and reducing water consumption. Initially, by considering the minimum values of the cropping pattern and using various computational steps, the maximum net economic profit and the amount of stored water were calculated through a bi-objective optimization method. In the next step, the net economic profit obtained from the stored water was optimized using a single-objective optimization method. Finally, the best computational step was selected by combining the values of bi-objective and single-objective net economic profit.

The results showed that the computational steps for each year were not the same, and the maximum net economic profit obtained by observing the minimum allowable cropping pattern and stored water (bi-objective optimization) and the maximum net economic profit obtained by optimizing stored water in the previous step (single-objective optimization) were calculated with different computational steps, considering the calculated annual profit. The results obtained from the maximum net economic profit and stored water based on the minimum allowable cropping pattern, bi-objective optimization (maximum net economic profit and maximum stored water), and the results obtained from single-objective optimization of stored water were accumulated. By considering the accumulated net economic profit from bi-objective and single-objective computational steps, the best net economic profit was achieved. Furthermore, despite the reduction in cultivated area and allocated water in the agricultural years of 2019-2020 and 2020-2021, an increase in net economic profit was observed compared to previous years. In the year 2020-2021, due to the lower allocated water compared to previous years and the optimal cropping pattern being on a negligible area of the target year's cultivated area (83.85 hectares), the highest net economic profit was achieved with a 138% increase in profit. This was due to the increase in product prices compared to costs, resulting in an increase in income greater than the increase in costs.

The results of the present research showed that the use of multi-objective optimization methods has a good performance in order to obtain more economic profit by managing optimal allocated water consumption. Also, taking calculated steps by the officials and organizations of water users to manage the allocated water reserve and achieve the maximum net economic benefit will increase employment, reverse migration and achieve a higher level of social welfare. According to the observance of the minimum strategic cultivation of wheat and the increase of rapeseed and triticale fodder crops, the current model has an optimal and appropriate performance in terms of ensuring food supply chain security and the point of view of non-agent defense.

In the mountainous forests of northern Iran, without the help of nature and the intervention of experts, there is no possibility of early stabilization of shallow landslide spots where the rich soil of the forest is often wasted due to leaching. Therefore, in this situation, timely implementation of soil protection techniques and identification of their effectiveness becomes necessary, especially in the steep sideslopes of forest roads.

In this research and along the network of forest roads of Shastkalateh plan of Golestan province, the location of earthen seideslopes, including cutslope and fillslopes with a slope of 45-60 degrees, was recorded with the help of GPS. Then, hydromulching operation with hydromulch, absorption bag, geo-cell, stabilization with geotextile, dead hedge, and alive hedge with 4 repetitions were carried out on the road sideslopes. 4 replications were also examined as controls without any treatment. Sampling was carried out by placing traps in the side air and at the beginning (retention trap) and the end of treated and control plots. At the end of a 6-month period (autumn and winter), five samples of sediments accumulated in the traps were collected to measure the amount of nitrogen, phosphorus, potassium and organic matter elements. The amount of absorbable potassium was determined by the ammonium acetate and chloride method, the amount of absorbable phosphorus was determined by the spectrophotometer method, the percentage of total nitrogen was determined by the Kjeldahl method, and the percentage of organic matter was determined by the nitration method.

The results showed that the loss of soil nutrients, especially organic matter and nitrogen, from the surface of the cutslope was more than that of the fillslope. While in geo-cell treatment, there was no significant difference between the loss of soil nutrients from the surface of cutslope and fillslope. The highest loss of soil nutrients was observed in the control treatment. In total, geo-cell treatment (83.7 percent) and alive hedge (75.2 percent) are the most efficient treatments, and hydromulch treatment (45.3 percent) and absorption bag (53.2 percent) are the most ineffective treatments in order to control nutrient loss.

Soil stabilization methods can preserve soil nutrients, prepare a suitable substrate for the regrowth of native plants, preserve soil microorganisms, and increase the resistance of road construction with minimal costs. The ability of soil protection methods to prevent runoff, sediments and loss of nutrients is different, and this depends to a large extent on the climatic conditions of the region. It was concluded that geo-grid and water absorption bags can be used to control soil nutrient loss from forest road sideslopes.

One of the national and strategic plans of the water sector is the restoration and resilience plan of groundwater. The aggravation of water level drop and reservoir deficit in aquifers has led to the prohibition of more than 405 areas out of 609 study areas in the country. In this connection, the Ministry of Energy, by defining the resilience plan, artificial feeding and flood spreading in 2004, compiled its programs in the field of improving the condition of groundwater resources. Again, with the active participation of the Supreme Water Council in the 11th government, the restoration and resilience plan of the country's groundwater resources including 15 projects was approved. Review of recent studies that have been conducted in different plains of the country; It shows that the resilience of water resources has had different effects on the condition of groundwater resources in different regions; Considering that the effects of the resilience plan on the study areas of South Khorasan have been less investigated; Therefore, in this research, the effects of the resilience project in the study area of Boshroyeh have been investigated, emphasizing the installation of smart meters.

In order to investigate the issue, the statistics of 16 observation wells and the rainfall data of Boshroyeh Plain were used in the time range of 1996-2021, and in order to investigate the fluctuations of groundwater, the statistics of the groundwater level of the observation wells in the year were used. Before (2009-2015) and after the installation of the smart meter (2015-2021) were used and the hydrograph of the aquifer wells was drawn and analyzed in the period of 2007-2022. Then, maps of groundwater level and confluence were drawn and analyzed using Arc GIS software and kerging interpolation in the years before and after the installation of the smart meter.

The average drop of the groundwater level in the period of 2009-2015 before the installation of the smart meter was 3.21 meters and the average drop of the groundwater level in the period after the installation of the smart meter in the period of 2015-2021 was reduced to 2 meters. ; In other words, after the installation of the smart meter, the water level has decreased. The decrease in the volume of aquifer in the period of six years after the installation of the smart meter compared to the decrease in the volume of the aquifer in the period of six years before the installation of the smart meter is equal to 67.6%, which indicates a decrease of 32.4% in the decline process The volume of the aquifer in the period after the installation of the smart meter is compared to the same period before their installation.

The results showed that during the statistical period of 2009-2021, the severity of the water level drop was not the same, so that from 2015 onwards, which was the year when the installation of smart meters was completed, the drop of the groundwater level faced a lower slope. Also, the decline in the volume of the aquifer in the time period after the installation of the smart meter has been lower compared to the same period before the installation. Also, the amount of saved water due to the installation of smart meters has had a significant economic value.