مجله سامانه های سطوح آبگیر باران
سال یازدهم شماره 4 (پیاپی 39، زمستان 1402)

Several factors can cause people's lack of participation in watershed management projects. The current research aims to identify, classify, and prioritize the Bushkan watershed in Bushehr province from the point of view of watershed residents and experts. For this purpose, these factors were identified and classified first. Then, paired comparison and Likert questionnaires were prepared as measurement tools, and the validity of the questionnaires was confirmed based on experts' opinions. After completing the questionnaires, hierarchical analysis was used by 34 expert experts to prioritize the indicators, using the fuzzy hierarchical analysis process. In order to prioritize indicators and sub-indices affecting the lack of sustainable participation of people in watershed projects from the perspective of the watershed residents, a questionnaire with a Likert scale was used as a measurement tool. After measuring the validity and reliability of the survey questionnaire It came to fruition. The sample unit is a rural household, and Cochran's formula was used to calculate the sample size. Based on the number of households in this district (678 households), 245 heads of households completed the questionnaire. The results showed that in the Bushkan watershed, there is a difference between the views of experts and people regarding the indicators of people's non-participation, in such a way that the experts give the design-implementation indicator the first priority, while the people living in the watershed give the education and promotion indicator the best priority. People know about non-participation. The ranking results of the sub-indicators showed that both groups of experts and local communities believe that "the lack of education of the watershed residents regarding the relevant plans and objectives" is the main reason for the lack of participation of local people in watershed management projects. From the point of view of experts, "non-use of local force in the implementation of projects (employment)" and from the point of view of people, "non-use of local promoter group" is in second place.

It is necessary to collect rainfall data for hydrological and hydrometeorological studies. To prepare this data, it is necessary to observe and record it at meteorological stations. In the process of determining rainfall amounts using rain gauges, various types of errors can occur. Therefore, some errors may be introduced into the final data during the observation and recording process. As a result, quality control of rain gauge data is of utmost importance. The objective of this study was to investigate the homogeneity of annual rainfall data at 61 rain gauge stations in the province of Chaharmahal and Bakhtiari and at 82 rain gauge stations in the province of Khuzestan. To detect the heterogeneity of the data, Pettit and Buishand's non-parametric tests were compared. The results indicated that, according to the Pettit test, the data of seven stations in Chaharmahal and Bakhtiari, and 24 stations in Khuzestan provinces are heterogeneous among the investigated stations. According to the Buishand test, there were significant differences in rainfall data among the three stations in Khuzestan province. Consequently, the data appears to be heterogeneous. It should be noted that even though both of these tests are used to test the homogeneity of rainfall data, the Pettit test involves two hypotheses with different continuous distributions. The Pettit test is considered more reliable in detecting homogeneity in comparison to the Buishand test in the study stations.

Since the increase in the depth of groundwater and its intensification can indicate serious limitations in the exploitation of these resources, predicting the changes of this parameter plays an important role in managing these resources and preventing possible damage to them. For this purpose, the use of smart methods has been strongly recommended by researchers. In this research, the methods of multilayer perceptron neural network (MLP), fuzzy inference system (fis), adaptive neural fuzzy inference system (ANFIS), and the combined method of fuzzy neural inference system and particle swarm optimization (ANFIS-PSO) were used for simulation of groundwater Fluctuations depth in Haji Abad area between March 1995 to October 2022 on a monthly scale. The training and testing phases were done with 75 and 25 percent of data, respectively. To measure the accuracy of the models, root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute value of error (MAE) indices were used. The best results in the training phase are related to ANFIS-PSO, ANFIS, and MLP models, respectively. Simultaneously with the training of the mentioned models, the testing stage of said models was also implemented. Finally, the best results in this stage belonged to the neural network model with time delay [1 3 5], the ANFIS-PSO model with time delay [1 2 3], and the neural network model with time delay [1 2], respectively. The accuracy indices in the test stage for the best models are (0.1871, 0.1865, 0.1857) for RMSE, (0.7402, 0.6715, 0.6684) for MAPE, and (0.1326, 0.1238, 0.1198) for MAE, respectively. These values show that all three models have an error of less than 20 cm, an error percentage of less than 0.75%, and an absolute error of less than 14 cm, which indicates the acceptable accuracy of these models. Also, the coefficient of determination obtained from the regression relationship of the calculated and measured values of the groundwater depth in the test phase for all three models is around 0.82, which indicates a relatively high linear relationship between these two parameters.

The location on the dry belt of the world and the recent droughts have doubled the need to increase water efficiency, especially in nature restoration projects, including the green belt projects of the country's big cities. Therefore, research and assessment to find ways to optimal the use of water and reduce the water requirement of the plants are among the essential needs of these areas. Based on this, the current research aims to evaluate the effectiveness of stone and pebble as well as plant residue mulches and on soil moisture retention in cultivation pits in the green belt of southern Mashhad. For this purpose, three irrigation treatments in three blocks with three treatments of stones and pebbles mulch, plant remains mulch, and the control in a total area of 1.5 ha with nine species (Celtis caucasica, Colutea buhesi, Contoneaster nummlaria, Kochia prostrata, Fraxinus excelsior, Morus alba, Cupressus sempervirance, Paliurus spina christi, Hypophae ramnoides) and four replications were cultivated in each block. Plots were irrigated with a specific plan and humidity and temperature of the soil, as well as the survival rate and growth of the cultivated species were regularly measured during the research. Then the effects of the used mulches on the storage of humidity and temperature and the establishment and growth of the plants were analyzed and investigated. The results showed that the treatment with stone mulch showed better growth in most of the species with a significant difference of 99%. Besides, there was a significant difference with other treatments in terms of moisture retention of the plant mulch in a few days after irrigation. Still, after these days stone mulch showed better performance on moisture retention. No significant difference was observed between the treatments in terms of plant survival. Finally, according to the obtained results and since stone mulch is preferred both in terms of preparation, implementation, and durability in the field, it can be said that stone mulch would be a better suggestion.

In recent decades, the problems caused by the occurrence of droughts on one hand and destructive floods on the other hand, have led the country's water resources managers and those involved in the implementation of flood-spreading and artificial feeding plans. The use of water harvesting structures as one of the solutions to reduce flood damage along with the optimization of flood exploitation in the restoration of natural resources has been common for a long time in different parts of the world. Considering that by carefully designing and implementing these measures, the possibility of flooding and damages can be significantly reduced and the underground aquifers can be fed. In this research, the integration of geographic information systems (GIS) and the best-worst method (BWM) was used to determine suitable flood-spreading areas. Twelve influential criteria were identified based on the opinion of experts in the region for flood-spreading, and the weight of each criterion was calculated using the BWM, and the permeability criterion with 0.33 had the most impact on location. The information layers were prepared in the GIS environment for each criterion, finally, by combining the information layers, the entire region was zoned according to the weighted criteria. The results showed that most of the area is in a completely unsuitable condition with an area of 298,725 ha, unsuitable with an area of 21,000 ha, an average condition with an area of 14,300 ha, as well as suitable and completely suitable areas with an area of 45,811 ha. In addition, permeability, slope, and geology respectively with weights of 0.5, 0.186, and 0.13 have obtained the first to third ranks.

The construction of rainwater catchment surfaces as a solution to the lack of livestock drinking water in the northern pastures of Golestan province has been less investigated. In the current study, a Geographic Information System (GIS) has been used to identify suitable areas for collecting rainwater in Kalaleh pastures. Among the effective factors in choosing the appropriate site for the construction of rainwater catchment surfaces the criteria of land use, soil depth, distance from fault, slope, distance from waterways, and distance from cattle breeding were selected. Then, with the help of Boolean logic, the weighting of the selection criteria was carried out. In this method, the unsuitable and restrictive areas were assigned a weight of zero, and suitable areas were assigned a numerical value of one. Prone and non-prone areas for rainwater collection were identified by combining information layers. The results indicated that among the selection criteria, the most restricting factor in the study area was due to the slope which made 94.7% of the unsuitable areas for the construction of a rainwater catchment system to be recognized and 3.5% of the pastures have the necessary potential to implement the system. This result is not certain and achieving a desired result requires the existence of acceptable information and data and the selection of proper methods, techniques, and criteria, the way of utilizing them in the studies of identification of prone sites for the construction of catchment systems seems necessary.