نشریه پژوهش های آبخیزداری
پیاپی 136 (پاییز 1401)

Issues related to implanting national policies for reducing natural hazards in developing countries are seen as an important and worrying challenge. In this research, the factors affecting the vulnerability, hazard, and risk of groundwater salinity in the southern plains of the Bakhtegan watershed with an area of 6137 km2 were assessed using statistical index models, Classification and Regression Tree (CART), and fuzzy analytic hierarchy process (FAHP). At first, by assessing the data of groundwater quality from 124 wells in the watershed, the points of groundwater salinity occurrence (based on the electrical conductivity parameter) were recognized. After calculating and preparing basic maps related to each of the indicators, we looked for a way for selecting indicators to identify key indicators. To this end, the recursive feature removal (FRE) method was utilized for finding and selecting the main combination of indicators to prepare a map of groundwater salinity hazards. Accordingly, after determining the significance of each indicator by calculating the jackknife test, the salinity hazard map of groundwater was predicted using two models (SI) and (CART). Also, the degree of vulnerability of the region to the salinity of the groundwater was determined using the (FAHP) process. The sensitivity, specificity, accuracy and kappa coefficient statistics were used to evaluate the performance of two-variable models. Also, in order to evaluate the performance of multivariate models, fore criteria of coefficient of determination (R2), conformity coefficient (CC), Kling-Gupta efficiency (KGE), and correlation coefficient (COR) were used. The results of groundwater salinity class risk study showed that 4.13 square kilometers of irrigated areas are in a very high-risk class, which among the various land uses with the highest vulnerability is related to this type of land use. Based on the results of this study, it can be stated that the development of comprehensive management plans, monitoring of executive activities, including change of uses, or taking into account the prevailing conditions in the implementation of rehabilitation operations can help control and manage risk in these areas.

Analysis of statistical relationships and quantitative factors and parameters affecting the production of sediment and soil loss is one of the problems of the watershed. The purpose of this study is to model the relationship between sediment production rate using MPSIAC experimental model factors and observed sediment rate using geomorphic parameters and their relationship with sediment yield in the watershed. Rastamabad watershed of Ilam province was selected by a simple random method with four sub-watersheds identified and equipped with a hydrometric station from the southern basins of Ilam province. In this study, an experimental model was used to estimate the sediment yield. Flow and sediment data of four hydrometric stations and 12 meteorological stations from 1991 to 2020 for 30 years were prepared by the Regional Water Company and the General Meteorological Office of Ilam Province. Physiographic information of the sub-watersheds was calculated from topographic maps with a scale of 1.25000 and the geomorphic features of the sub-watershed were extracted from the digital elevation model. Using factor analysis and cluster analysis, influential factors and variables were identified and sub-domains were classified and divided into homogeneous regions. In order to investigate the correlation between independent and dependent variables, the data normality test was performed by Shapiro-Wilk and Kolmogorov-Smirnov tests in SPSS software. The statistical multiple regression method was used to analyze the relationship between experimental model factors and geomorphic variables with sediment yield of each watershed. The results showed that the sediment yield had a positive correlation with geological factors, land use, upland erosion, river erosion and topography of the watershed and was significant (P≤ 0.001). The amount of observed sediment had a positive correlation with slope, circulatory ratio, rainfall, topography, and area of the watershed and was significant (P≤ 0.001). In order to influence the factors and variables on the amount of sediment in the sub-watersheds, the method of principal component analysis and cluster analysis were used. The results showed that the land use explained 25.24% of the variance of all research variables. Finally, the three factors of land use, upland erosion, and geology, and two parameters of circulatory ratio and slope were explained at 86%. Of the variance of all research variables.

Landslide is one of the most destructive types of erosion on slopes, which causes a lot of financial and human losses. Identifying the effective factors in landslide occurrence and preparing a landslide hazard zonation map is one of the basic tools to manage and reduce potential damage. The purpose of this study is to evaluate the efficiency of fuzzy gamma operators and landslide hazard zonation in the Qezelozan watershed of Qazvin province. The landslide inventory map and also 11 effective factors including slope, slope direction, altitude, land use, lithology, distance to road, distance to stream, distance to fault, earthquake acceleration, precipitation, and maximum daily precipitation were first prepared. Then, the values of frequency ratio and fuzzy membership were calculated for each class of effective factors. In the next step, the landslide susceptibility maps were produced using fuzzy gamma operators. A total of 17 landslides were identified, 70% (11 landslides with an area of 153 hectares) of which were used to model and 30% (6 landslides with an area of 60 hectares) of which were used to evaluate the results of the models. The evaluation process using Density Ratio and Sum of Quality indices showed that the gamma of 0.96 has higher accuracy than other gamma values in the study area. The landslide hazard zoning map of the superior model will be useful in land use planning and reducing the landslide risk of the region.

Groundwater is exploited uncontrollably due to population growth and industrialization in different parts of the world. The purpose of this study is to evaluate the groundwater potential by advanced machine learning algorithms using topographical, hydrological, environmental, and geological criteria. To do this, three advanced machine learning algorithms were used, including Boosted Regression Tree (BRT), Logistic Model Tree (LMT), and Random Forest (RF). Therefore, for implementation, geo-hydrological data of 37 groundwater wells in Birjand plain of South Khorasan province were collected and randomly selected in a ratio of 70 to 30 were divided into training and validation data sets. Finally, groundwater potential maps were prepared using BRT, LMT, and RF algorithms. In order to validate the groundwater potential prediction algorithms, the area under the curve (AUC) and the statistical criteria of positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy were used. The results showed that the LMT model (AUC = 0.865) has a better performance than the BRT and RF models in predicting the groundwater potential of the study area.

One of the most critical environmental problems globally is soil erosion, which leads to several problems in the field of economy and sustainable development. This study aims to use new methods in soil science studies of the paired sub-catchments to update service descriptions in Dehgin, Hormozgan Province. For this purpose, to select sampling sites of pedons and soil samples, the conditional Latin Hypercube sampling (cLHS) method was used using environmental covariates (digital elevation model), which derivate from unmanned aerial vehicles (UAV), phantom4 pro, and multirotor. The results showed two main land types in the study area, including hills, plateaus, and high terraces. Soil development in the region is mainly influenced by elevation, and the soils of the hill lands are directly related to the type of parent material and slope percentage. In the areas containing wooded pasture and areas with silt loam texture class, the aggregate stability values are higher, which are auxiliary covariates essential and influential parameters in the aggregate stability map. The RMSE and R2 values are 0.32 and 0.26, respectively, for the evaluation map criteria. The values of cation exchange capacity (CEC) and nitrogen in both control and sample sub-catchments are small and the maximum amounts of soil organic carbon in the sample sub-catchment are higher than in the control sub-catchment. The use of new methods including UAV, accurate sampling methods using high-resolution environmental covariates, and digital soil mapping can reduce the number of soil samples, increase the accuracy of output maps, and provide more accurate erosion estimation results, which has reduced time and cost compared to the old methods.

Long-term monitoring as well as the impacts of climate change on sand dune mobility play a crucial role in reducing their adverse effects, including creating dusty days, potential risks of reduced air quality, and other environmental and health risks. The main objectives of this study were to investigate the dust phenomenon and its origin, to analyze the mobility status of sand dunes using two indicators, to determine the effect of climatic conditions on mobility, and also to predict the effects of changes in climate parameters on sand dunes mobility in the strategic area of ​​Sarakhs, located on the northeastern border of the country, for a period of 27 years. The results on the number of dusty days showed that the local dust events at Sarakhs station had a significant increasing trend. The results of sand dune mobility using Lancaster and modified Lancaster indices showed that there was a significant relationship at 99% level with dusty days, based on which the ability to use the modified Lancaster index in this area is raised. It can also be acknowledged that in the study region, increasing environmental drought coming with the mobility of sand dunes increases the risk of desertification. Also, the results of sand mobility sensitivity analysis showed that the combined effect of the frequency of erosive winds or the potential of sand transport and evapotranspiration has the greatest effect on reducing and increasing sand activity in the future.

Neotectonic activity plays an important role in increasing the occurrence of landslides. The purpose of this study is to investigate the relationship and the extent of landslide occurrence from active tectonics in the Kheirabad River Basin River. For this purpose, neotectonic activity in four sub-basins of this river and based on six morphotectonic indicators; V/A, Bh, P, BR, Ff, and Tf were calculated. The mentioned indices were extracted in each sub-basin using Arc GIS 10.4 software and digital elevation model (DEM 10 M) as well as the Archydro plugin. Each index was divided into three categories in terms of tectonic activity, and finally, based on the segmentation of each index, the level of tectonic activity of the region was determined on the basis of the index of relative tectonic activity index (Iat). And the area of the study was divided into 3 categories. The results showed that neotectonic activity is very high in Shahbahram sub-basin and high tectonic activity in Dehdasht and Sarpari sub-basins and low tectonic activity in Kheirabad sub-basin., Finally, by cross overlaying of the landslide inventory map to the relatively active tectonic zonation map, the percentage of landslides that occurred in each tectonic region and the role of active tectonics in landslide occurrence was determined. The results showed that 74.69% (587.75 ha) of landslides occurred in the area with high tectonic activity, which covers only 33.11% of the total area of the study basin. Accordingly, 199.09 hectares of landslides occurred in areas with moderate and low tectonic activity, which constitutes more than 66/89% of the total basin. This distribution indicates the dependence of the landslide occurring with active tectonics, in other words, the role of active tectonics in the occurrence of landslides.

The weighting factor of the sediment connectivity index (IC) is a challenging component of this index and must be properly explicated for different situations. The aim of this study is to develop a proper weighting factor for calculating IC based on experiments on three tilled soils from dry-farming fields in Kouheen, Sararud, and Gachsaran regions under a 111 mm/h rainfall simulation using a 5.8 m2 laboratory flume with a slope of 12%. For each soil sample, two rain events (R1 and R2) were simulated with an interval of 4 hours, and the soil losses were measured. In addition, a digital elevation model with a pixel size of 2 mm was prepared by accurate photography after each rain followed by calculating the IC based on roughness coefficient (RI) as the weighting factor. Measured soil losses of two rainfalls were 4796 and 3909 g for Gachsaran soil, 3465 and 2464 g for Kouheen soil, and 2679 and 2105 g for Sararud soil, respectively. The result shows that with increasing IC based-RI, soil loss was decreased which indicates the inadequacy of RI alone as a weighting factor. To develop a new weighting factor, several physical and chemical characteristics of soils were used in combination with RI. A comparison of the results showed that the IC based on a weighting factor obtained from the combination of mean weighted aggregate diameter, penetration resistance, and RI, has the highest coefficient of correlation with soil losses (0.92, P <0.01). Examination of the new IC maps showed that the IC in R2 increased compared to R1. It is recommended to study the possibility of using the developed weighting factor in calculating the IC of hillslopes after tillage in determining critical points in the hillslope.