Integrated Watershed Management
Year:2024 | Volume:4 | Issue:3|Papers Count:6

strong>Introduction: The human environment has experienced significant changes due to issues such as industrial development, technological advancement, population growth, and unsustainable extraction of non-renewable resources. Natural resource management includes the coordination of human interactions with the natural environment Desertification is a complex issue, which is caused by both natural processes and human actions. This includes land degradation due to various factors such as wind and water erosion, destruction of vegetation and reduction of water resources. The purpose of this research is to identify the criteria and indicators affecting the non-sustainable participation of rural communities in desertification projects, to prioritize these criteria and indicators based on the opinion of experts and to provide solutions to increase the participation of local stakeholders. Materials and methods: Garmsar, which is located in the west of Semnan province, is one of its eight cities. The communities of this research, the villages of Ghiyath Abad, Shorkazi and Mohsen Abad are part of Garmsar city, which have desertification plans. Several factors are involved in the non-participation of people in desertification projects, which can be classified in the form of relevant criteria and indicators. These factors are identified and classified based on library studies, questions from regional experts. In order to prioritize the criteria and indicators effective in the non-participation of people in desertification projects, respectively, questionnaires related to the process of hierarchical analysis (AHP) and a questionnaire with a Likert scale were used as measurement tools. Also, before prioritizing the criteria of the relevant indicators, the validity of the questionnaire was approved by the experts. In this research, there will be a survey of experts from the departments of forestry, watershed management, pasture and desert, land assessment, etc., of the general natural resources department of the province and Garmsar city, with an experience of more than 15 years. After completing the hierarchical analysis questionnaires by 30 expert experts, the hierarchical analysis process was used to prioritize the effective criteria on people's non-participation in desertification projects. Finally, Friedman's test was used to prioritize the indicators and determine their relative importance on people's non-participation in the combating desertification projects. Results and Discussion: According to the results, the educational criterion is the most important criterion of people's non-participation in desertification projects from the point of view of experts (0.48), and the planning (0.29), economic (0.13) and social (0.07) criteria are in the next stages. are priority. From the point of view of experts, the index of "non-use of local promoter groups", with an average rank of 11.72, has the highest relative priority, and the index of "lack of trust in the results of projects" with an average rank of 6.15, has a lowest relative priority in the non-participation of people in the desertification projects of the region. Regarding the findings related to barriers to participation in Ghiyathabad village, the index of "non-use of local promoter groups" was identified as the first priority with a rank of 10.73. In the case of Mohsen Abad village, the index of "weak role of local media in promoting desertification projects and encouraging community participation" has been assigned the highest priority with a rating of 10.50. Also, the findings of Shur Ghazi village show that the index of "low level of literacy in the local community" with a rank of 9.88 is a major concern. Conclusion: In general, the successful implementation of desertification projects relies heavily on achieving maximum participation of local residents during the implementation and maintenance phases. This research was carried out with the aim of identifying factors affecting the non-participation of villagers in desertification projects. As a result, experts emphasize the importance of education and increasing public awareness to increase participation. Related educational videos, as well as visiting successful projects in other regions, accompanied by elders and members of the village council, and sharing their field experiences and observations with the villagers will increase their knowledge and awareness about the employment capacities in this project. It will be In addition, such initiatives will help reduce the adverse effects of floods, dust storms and droughts, thereby improving the overall well-being of residents.

Extended Abstract Introduction: Soil erosion and water crisis are among the most significant threats that endanger the security of water and soil in the country. In order to address these problems, measures are being taken to soil and water conservation. The most important stage in implementing these plans is the structural measures of watershed management, which involves identifying the correct locations for implementing these plans. Proper site selection of check dams has a significant impact on reducing the cost of watershed activities and increasing their effectiveness. Traditional methods of locating check dams based on more information layers and the need for their integration and analysis are difficult and may result in errors, while modern and highly efficient data mining methods based on the quality and quantity of data and machine learning have been introduced. In this study, the maximum entropy (MaxEnt) model was used to locate check dams in the Dehdar Taleghan watershed.   Materials and methods: The Dehdar Taleghan watershed is located in the north of Alborz province, with variable elevation from the highest point at 4050 meters above sea level to the outlet of the watershed at 2248 meters. There are two villages located within the basin, which covers an area of 4780 hectares. The locations of existing and proposed check dams were extracted based on the database of implemented check dams in the Natural Resources and Watershed Management head office of Alborz province and the mechanical check dams’ manuals of detailed-executive studies. In this study, 14 factors that affect the placement of watershed check dams including DEM, slope, distance from stream, roads, and faults, density of stream, roads, and faults, lithology, land use, stream power, stream rank, flow accumulation, and precipitation were used to determine suitable locations. The multicollinearity was checked using the variance inflation factor (VIF) and tolerance index. After confirming the absence of multicollinearity between variables, the existing check dam’s points were randomly divided into training data (70%) and validation data (30%). The importance of each variable in explaining the model was determined using the MaxEnt model and the Jackknife plot, which was performed using MaxEnt software. In this study, the performance of the model in the training and validation stages was evaluated using the receiver operating characteristic (ROC) curve and its area under the curve (AUC).   Results and Discussion: The results showed that there is no linear relationship between the factors, and therefore all factors were used in the modeling process. The results of the Jackknife plot showed that distance from streams, slope, flow accumulation, stream order, elevation, mean precipitation, and lithology were the most important factors affecting the visibility of check dams, and they had a significant impact on predicting areas with potential for check dam construction. The accuracy of the model prediction was excellent in both the training (0.959) and validation (0.961) stages. Field surveys confirmed that the model accurately identified critical streams in terms of flooding and sedimentation, with a total of 30.3 kilometers of critical and supercritical stream identified. During various field visits, 11 check dams were identified in the studied streams. It is worth noting that the final map (critical areas) and the visibility of check dams, as assessed by consulting companies in detailed executive studies, had a 92% level of agreement, demonstrating the high accuracy of machine learning models for predicting check dam’s visibility.   Conclusions: In this study, a map of suitable areas for check dam’s construction in the Dehdar Taleghan watershed was prepared using the MaxEnt model, taking into account influential environmental variables. The ROC curve showed that the model's accuracy in estimating areas with potential for check dam’s construction was excellent in both the training and validation stages, indicating excellent model performance. Based on the results, it can be said that the MaxEnt model has a high ability to determine areas with potential for check dam’s construction. Due to its speed and high accuracy, the model is recommended for use in similar studies, especially in developing countries facing a shortage of facilities and financial resources. Combining geographic information systems with modern machine learning models to determine areas with potential for check dam’s construction, especially in developing countries like Iranis recommended.

strong>Introduction: The industrialization of communities has led to an increase in greenhouse gases over recent decades. This increase has caused the warming of the earth's atmosphere, and it affected other components of the climate system and led to climate change. The evidence confirms that the global average temperature of the Earth is increasing, especially in recent years. Moreover, precipitation intensity has changed over time. It is expected that changes in temperature and precipitation will cause a series of climatic extreme events. Therefore, it is an undeniable fact that the intensification of global climate change has affected the development and survival of mankind. The purpose of this research is to investigate and predict these changes in the future decades in the Taleqan watershed, Iran.Materials and methods: The study area, Taleqan watershed with a mountainous topography, is located in the northwest of Alborz province, Iran. The annual precipitation and temperature of the region are 485.8mm and 11.4℃, respectively. To estimate and generate data for the future period (2021-2040), we used daily data from regional stations, including precipitation data from the base period (1979-2014) and average temperature data from the base period (2003-2014). We also utilized output data from the General Circulation Model (CanESM5) under climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). CanESM5 is a global model developed for simulating future climate change and developing seasonal and decadal forecasts. CanESM5 is usually used for large-scale projections, therefore, SDSM is chosen to downscale climate data. The changes of average precipitation and temperature parameters for three future periods (2021-2040, 2041-2060, and 2081-2100) were evaluated and RMSE, MAD and R were used to evaluate model accuracy.Results and Discussion: The greatest increase in precipitation in Armot station is in March, May and November and the greatest decrease in precipitation in September and October is predicted under the SSP scenarios for the periods 2021-2040, 2041-2060 and 2081-2100. In Sakranchal station, the highest increase in precipitation is in three periods of March, February and May, and the highest decrease is in September and October under the SSP1-2.6, SSP2-4.5 and SSP5-8.5. The highest increase in precipitation in the Zidasht station is in March, May and November, April and December under the SSP scenarios. Also, the biggest decrease is in September under the SSP scenarios for the three forecast periods. In Gateh Deh station, the highest increase in precipitation during the periods of 2021-2040 and 2081-2100 is related to March, May and November, and for the period of 2041-2060, it is related to the months of March, February and May under the SSP5-8.5. During the periods of 2021-2040 and 2041-2060, the greatest decrease will be in September under the SSP scenarios, also in the period of 2081-2100, the greatest decrease will be in July under the SSP scenarios and in October, under the SSP5-8.5. During three periods, the greatest increase in Jovestan station precipitation is predicted in February, March, May and November. Also, the greatest decrease in the period of 2021-2040 will be in September and August under the SSP5-8.5 and in the periods of 2041-2060 and 2081-2100 in October and July under the SSP1-2.6. Based on the results of the temperature forecast in Zidasht station during three periods, the average and average maximum temperature in January and February under the SSP scenarios have a decreasing trend and other months show an increasing trend.Conclusion: The results show that precipitation has a decreasing trend in some months and some increasing trend. The obtained results indicate that the precipitation and temperature variables in the periods of the 2021-2040, 2041-2060 and 2081-2100 under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 will experience an increasing trend compared to the base period. The highest increase in temperature and precipitation is in the period of 2021-2040 under scenarios SSP2-4.5 and SSP1-2.6, respectively. This study demonstrates that Taleqan watershed will be vulnerable to future climate change. An increase in temperature can cause snowmelt and reduce snow storage. The stability time of water reserves in the watershed will be reduced. Precipitation changes in the region can alter the precipitation pattern from snow to rain. This reduces surface and underground water, and can affect crop yield.

strong>Introduction: In many geographical and scientific phenomena such as hydrology and meteorology, where sampling is costly, observations are often sparse and pointwise. Selecting an appropriate interpolation method is crucial for water resource management, as it significantly impacts accuracy and cost reduction. Interpolation, utilized across various sciences, primarily aims to predict unknown values through a range of mathematical and statistical models. Ensuring the interpolation method and its correctness saves money and increases the accuracy of decision making. In general, a method that provides an optimal solution in all phenomena and locations is not found. Considering the environmental conditions of the region various methods should be assessed to determine the most optimal approach. Materials and methods: The study area of Gonabad, with an area of 1805.6 km2, of which 1048.7 km2 are plains and the rest are highlands. In this research, the main goal is to evaluate the common methods of interpolation in the plains of Razavi Khorasan province. The plains of Gonabad and Sarakhs were studied due to their different hydrological and hydrogeological conditions; Also, in this review, inverse distance weighting (IDW) methods, kriging (simple, ordinary), cokriging, radial basis functions, and Thiessen's method were selected as interpolators for the study; and all these methods were evaluated by cross-validation. Results and Discussion: In the present study, two methods of interpolation, IDW and Kriging, produced much more appropriate estimates than the rest of the studied methods. In comparing these two methods, IDW, as a classic method, requires fewer parameters and is simpler to implement. However, since it does not take into account the arrangement of data and the correlation between them, it is less accurate than kriging. On the other hand, geostatistical methods such as kriging, while offering better accuracy, require statistical tests, data transfer, data distribution, and spatial structure analysis due to hypotheses such as normality of data. This complexity and time-consuming nature can lead to errors. In this study, to assess the groundwater level under different conditions, information from two months of the year was utilized in each time period as representative of wet (February) and dry (August) periods. Contrary to previous ideas, due to the dry climate and low rainfall in the plains of the province, no significant difference was observed between these two months. Contrary to previous assumptions, the dry climate and low rainfall in the plains resulted in no significant difference between these two months. Conclusion: Applying interpolation methods to two different time periods was conducted to investigate long-term decline. In the Gonabad Plain, the best methods were identified with RMSE values ​​of 11.96, 14.02 and 14.49 meters for IDW, ordinary kriging and simple kriging, respectively Simple and ordinary kriging methods as well as IDW with RMSE values ​​of 1.41, 1.58 and 11.83 m, respectively, were introduced in the Sarakhs Plain as the most optimal methods. In depicting the zoning of groundwater levels, as illustrated at the end of each plain by the aquifer map and changes in water level, it was found that height fluctuations corresponded to the information available in hydrogeological surveys of each region to a large extent. Analyzing the forecast results during the years 2012 to 2016, the decrease in groundwater level, based on the best and most optimal methods applied in the study plains of Gonabad and Sarakhs, amounted to a negligible four, seven, and five meters. In the province's plains, the Sarakhs Plain aquifer exhibited higher accuracy than others. This was attributed to the correct arrangement and density of sampling stations in this plain.

Introduction: The salt domes of Semnan province are geomorphosites that have undergone many changes and transformations over time, influenced by internal and external factors. As a result of these developments, very diverse and beautiful geomorphological forms have appeared on the surface of these domes and the surrounding areas. The existence of such natural attractions has made this province one of the most important regions in the country for attracting private investors in tourism and developing infrastructure for the tourism industry. This research aims to first investigate the salt domes of Semnan province and then identify which domes can be converted into geomorphosites. For this purpose, two regions of the salt domes in Semnan province were considered as geostructures: the salt domes in the south of the province and the salt domes of Garmsar.   Materials and Methods: After determining the locations of the salt domes in these two regions on the province map using Google Earth, related shape layers, ArcGIS software, and the Geosite Assessment Model (GAM) and its modified model (M-GAM), the tourism value of the two regions was evaluated, and the limitations of these area were determined. These salt domes were quantitatively analyzed using 27 sub-criteria from the GAM and M-GAM models, supported by a questionnaire. Data from the questionnaire (168 participants, including 117 experts and specialists, and 51 tourists and visitors) were input into the models, and the results were compared. The total values (main and supplementary) were placed in the matrices of models and analyzed. Finally, the sites were ranked based on three key values in each geomorphosite: scientific, conservation, and tourism values. Since geomorphosites are closely related to these three values, the two studied geomorphosites were ranked accordingly by combining sub-criteria and detailed values from the two models.   Results and Discussion: Based on the final values obtained, the salt domes of Garmsar rank first, and the salt domes in the southern province rank second. The results showed that the final value of the salt domes in the south of the province and Garmsar in the GAM model is 10.5 and 13.25, respectively, while the final values in the M-GAM model are 5.66 and 6.61, respectively. By examining the results of this research and other studies, it can be concluded that most geomorphosites have a high value in terms of main values but a low value in terms of complementary and infrastructure values. This combination was equally observed in both models. In both models, Garmsar salt domes ranked first in scientific and tourism values, while the salt domes in the south of the province ranked first in conservation values. The primary reason for the higher conservation value of the southern salt domes and their lower scientific and tourism values is the poor access to these domes. The lack of suitable roads, the presence of a military zone in the south of the province, and the lengthy process of obtaining permission to enter the region are the main barriers to proper access. These access issues create several bottlenecks, including inadequate infrastructure, lack of accommodation, insufficient restaurant services, and a general lack of public awareness about the existence of these salt domes. Therefore, improving access to the salt domes in the south of the province and leveraging this natural resource's potential is crucial.   Conclusion: Due to the greater attention given to the salt domes in the south and west of the country, which are important for oil reserves and other economic resources, less attention has been paid to other salt domes. Based on the findings, it can be concluded that using the GAM and M-GAM methods together allows for better investigation of geomorphosites. Since geomorphosites are closely related to scientific, conservation, and tourism values, the 27 sub-criteria were categorized accordingly. This new composition can significantly aid geomorphotourism planning and prioritization. Since tourism transforms threats into opportunities, developing suitable infrastructures and investing in tourism can mitigate the negative aspects of salt domes.

Introduction: With the increase in population and the growth of industry and agriculture, the production of various types of human, industrial, and agricultural waste also increases. Improper disposal of waste can lead to environmental pollution and contamination of water resources. One common method of waste disposal is landfilling. A significant challenge of this method is finding a suitable location for the landfill, as the selection of an appropriate site depends on various criteria and factors. The use of Geographic Information Systems (GIS) and multi-criteria decision-making methods are effective and efficient tools for addressing this challenge. The aim of this study is to use GIS and fuzzy overlay methods to locate suitable landfill sites for municipal and industrial waste in Birjand. For the first time, this research uses spatial data layers for aquifer recharge areas, protective zones of drinking water wells, qanats, and springs in South Khorasan Province.   Materials and methods: This study combines GIS and fuzzy overlay functions to locate suitable landfill sites for municipal and industrial waste in Birjand County. The study area is Birjand, the capital of South Khorasan Province. The fuzzy overlay method utilizes a range of values between 0 and 1 to express the degree or value of set members. The study incorporates various criteria, including environmental, geological, infrastructural, and water resource factors, each comprising multiple sub-criteria. After preparing the necessary data in shapefile format, the Euclidean distance method in ArcMap software was used to create distance layers for each data point. These distance layers were then standardized using fuzzy logic through Python code. The fuzzy standardization of these layers was based on the technical evaluation guidelines for the disposal of ordinary and special waste provided by the Environmental Protection Organization. Finally, using the prepared fuzzy layers and fuzzy overlay functions including AND, OR, SUM, PRODUCT, and GAMMA, the final layer of suitable sites for municipal and industrial waste disposal was created.   Results and Discussion: The results of the overlay operation indicate that the OR and SUM functions did not yield satisfactory results for fuzzy decision-making. The results of the AND, PRODUCT, and GAMMA functions were classified into five categories based on the environmental and water resource risks associated with waste disposal in each area: very high, high, moderate, low, and very low risk. The results indicated that the area with very high risk for waste disposal was the same across all functions. The AND function provided the smallest area for low and very low-risk zones, whereas the GAMMA function provided the largest area for these zones. The results suggest that if the GAMMA function’s power is chosen between 0 and 1, the results will be a combination of the AND and OR functions.   Conclusion: Considering the need to place landfill sites near waste production sources to mitigate costs and ownership challenges, it was found that the GAMMA function provides the most suitable results for locating landfill sites for ordinary and special waste in Birjand. According to the results of this function, 3535.982 km2 of Birjand County's area (about 88%) is a prohibited zone for municipal and ordinary waste disposal, and 3670.934 km2 (about 92%) is a prohibited zone for industrial and special waste disposal. The remaining area, 368.7 km2 for municipal and ordinary waste and 233.7 km2 for industrial and special waste, is suitable for landfill site selection. It was also found that the current municipal waste landfill site in Birjand County is located in a prohibited zone. Therefore it is necessary to relocate it and select a new site based on the results of this study as soon as possible.