مجله ترویج و توسعه آبخیزداری
پیاپی 44 (بهار 1403)

Management and planning have different levels, of which the strategy is the highest level and has a long-term approach to resource allocation and decision making. Comprehensive watershed management is one of the reliable and accepted management approaches of the world community, which is a coordinated management of physical, biological, social and economic systems. The SWOT model is one of the strategic planning models. Due to its structural features and extensive capabilities, this model can easily be used in the management of diverse natural and human systems, including watershed management. SWOT analysis identifies threats and opportunities in the external environment of a system and to recognize internal weaknesses and strengths of that system. The correct identification of the problems in the field is also one of the most important steps in implementing this approach. The SWOT model can be used in comprehensive watershed management due to strategic planning because of its flexibility and ability to understand cause and effect relationships.

Water plays a vital role in human life and is essential for the survival of all living organisms. Managing water consumption is crucial for preserving water resources and preventing water scarcity crises. Various methods for water conservation exist, including the use of water-efficient equipment, repairing leaks, and educating the community about optimal water usage patterns. These actions can contribute to reducing water wastage and improving the efficiency of water resource utilization. Therefore, effective measures in the management and efficiency of water resources are essential. The research tool in this study is a questionnaire developed by the researcher, encompassing indicators affecting sustainable water resource management and development. Data analysis in this research utilized Pearson correlation, Kruskal-Wallis, and regression tests in SPSS version 25 software. The questionnaire results identified the risk management component as the most important indicator with an average of 7.4, while the research and education promotion component had the lowest importance with an average of 2.3 for sustainable water resource management and development in the country. The regression model of the research showed that, among the factors examined, water efficiency, community participation, and water supply for basic needs demonstrated a more significant impact and influence compared to other dimensions. Considering the research findings, it can be concluded that water resource utilization in Iran is not compatible with sustainable development, and deficiencies in water and groundwater management are among the significant factors contributing to water scarcity in the country. Therefore, implementing policies and initiatives that increase water consumption efficiency while simultaneously reducing negative environmental impacts is proposed as a managerial solution, along with the utilization of artificial neural networks for identifying water wastage sources in future studies.

Soil erosion is a global problem that reduce soil fertility and water quality Proper evaluation of the factor’s affecting erosion is the first step in choosing solution to reduce and control this destructive phenomenon. Important parameters that can be examined in this issue are soil texture, size and quality soil grains and type of bedrock, which can affect the soil erodibility coefficient. Fractal dimension (Df) of particle size distribution (PSD) and fractal dimension of aggregate size distribution (ASD) have been introduced as a predictor of soil texture-related properties. The purposes of this study were to investigate the relations between fractal dimension and various forms of erosion, including surface erosion, rill and badland, also to investigate the relations between fractal dimension and various forms of bedrock and to study the mean weight diameter (MWD), aggregate stability (AS) and aggregate degradation (PAD). Fractal dimension was computed by Yong and Crawford model using the data of particle size distribution experiments by the method of wet and dry sieve and aggregate size distribution by submerged sieve method. Bulk density by weighted method and specific surface area by EGME method were determined. The results of statistical analysis showed that different forms of erosion and bedrock are related to the fractal dimension. In other words, with the development of the erosion form, the fractal dimension increased, so badland had the highest and surface erosion had the smallest fractal dimension. Also, among various bedrocks, with decrease in resistance and increase in erodibility, Df was increased. In addition, fractal dimension had a positive correlation with MWD and AS and negative correlation with PAD. Finally, it can be concluded that due to the ease of calculating the fractal dimension of soil and its proper relationship with erosion, this index can be used in the quantitative and qualitative assessment of soil erosion.

In general, mining is associated with the destruction of natural resources and the environment, intentionally or unintentionally. This has gone so far that the supporters and advocates of natural resources and the environment consider mining and mining activities as one of the most important factors in the destruction of natural resources and the environment. On the other hand, the importance and role of mining and mining in the progress of the country and achieving sustainable development is not hidden from anyone. In other words, mining activities cannot be stopped, nor can the importance of natural resources and environment be ignored as one of the pillars of sustainable development. What is certain is the point of interaction and balance of these two categories of restoration and revitalization of mines. This means that after benefiting from small and large mines, it is necessary to restore and restore these mines with the aim of sustaining natural resources and the environment, and as far as possible, the destructive conditions and disturbances created should be returned to the previous state. Mining or close to the original state.

Climate change is one of the biggest challenges that humanity has faced in the 21st century, because it can have severe effects on water resources, agriculture, energy, and even human bioclimate. The purpose of this study is to investigate the changes of climatic parameters in four sites of Plour, Rineh, Kangarchal and Poshtkoh in Mazandaran province using general atmospheric circulation models. For this purpose, the data of IPSL-CM5A-MR and CSIRO-MK36 models under RCP4/5 and RCP8/5 scenarios have been scaled with LARS WG statistical micro-rotator to evaluate the impact of climate change. After carrying out calibration, verification and modeling of data in two stations (Abali and Kiasar), the effectiveness of the models in terms of the amount of compliance of the observed data with the simulated value using the RMSE index, R2, MAE was evaluated. The obtained results showed that IPSL-CM5A-MR and CSIRO-MK36 models obtain acceptable results. Considering that in Abali station, the amount of rainfall in the base period is 542.08 mm under RCP4.5 and RCP8.5 scenarios in both IPSL-CM5A-MR and CSIRO-MK36 models in the future between (2021-2060) will increase by 0.91-0.97 percent. The average maximum temperature will increase by 1.7-2.2 centigrade and minimum temperature by 1.56-1.88 centigrade compared to the base period. Accordingly, the amount of rainfall in the base period in Kiasar station is 528.46 mm, which under the scenarios of RCP4.5 and RCP8.5 in both models in the future between the years (2021-2060) is 96. 0-87% will increase. The average maximum temperature will increase by 1.72-18.2 centigrade and the minimum temperature will increase by 1.62-1.82 centigrade compared to the base period.

Qualitative modeling is one of the methodologies employed for assessment. However, skepticism about the outcomes of such models has led to a reduction in their consideration. Consequently, this study employs the qualitative model approach of PAP/RAC to predict and determine the status of water erosion risk in the Qarnaveh watershed. This approach is juxtaposed with the RUSLE model, and influential factors are identified using Bayesian Model Averaging (BMA) to enhance evaluation, delineate vulnerable zones more effectively, and propose viable water and soil conservation strategies. Research findings revealed a 20.56 Km2 difference in predictions between the two models. Nonetheless, a meticulous spatial examination of water erosion risk prediction maps from both PAP/RAC and RUSLE models demonstrates a spatial congruence of 67.74%, predominantly within the "very high" category. Furthermore, the analysis of influential factors (slope, lithology, land use, and vegetation cover density) on the erosion risk prediction map, employing the Bayesian averaging approach, underscores slope as the most significant determinant in the developed superior patterns. In the PAP/RAC model, it exhibits adverse effects in the "very low" category but exerts positive influence in the "moderate," "high," and "very high" categories. In view of the significant influence of slope across all categories, it is therefore necessary to carefully classify and assess regional characteristics for refined predictions. Consequently, based on research findings, the PAP/RAC model emerges as the most efficacious and straightforward qualitative model for estimating water erosion risk in data-deficient watersheds, with precise consideration of its influential factors aligned with Iranian context. This methodological approach, which integrates descriptive and integrative techniques, not only facilitates precise prediction, but also facilitates the formulation of effective water and soil conservation strategies, thereby contributing to the development of a comprehensive strategy for sustainable watershed management.

The main objective of this study was to develop machine learning models for predicting soil organic carbon (SOC) content in agricultural soils enriched with calcium-rich materials in the central region of Iran. Soil samples were collected from 336 points in agricultural lands with two land uses, namely cropland and orchard, from Alborz Province. The soil organic carbon was measured using the Walkley-Black oxidation method. Remote sensing data, obtained from various sources such as Landsat 8 imagery and MODIS sensor, were utilized for soil organic carbon analysis. Three machine learning models, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Gene Expression Programming (GEP), were employed in this study to estimate the soil organic carbon content. The results of this research demonstrated positive outcomes, indicating that the simultaneous use of Landsat 8 imagery and MODIS sensor data through the SVR model yielded better performance (R2 = 0.62, RMSE = 0.63, R2/RMSE = 0.98) compared to the use of each image separately. Therefore, this study concluded that the simultaneous synergy of remote sensing data from different sources (referred to as data synergy) can significantly improve the accuracy of predicting soil organic carbon content in calcium-enriched agricultural soils in the central region of Iran. It is recommended to use advanced fusion techniques and deep learning methods for combining features at a higher level.