مجله اکو هیدرولوژی
سال دهم شماره 3 (پاییز 1402)

According to the predictions outlined in global warming models, Iran, especially the plains of the central plateau, has been dealing with increasing temperatures and decreasing rainfall. These changes have had harmful effects on agricultural production and the overall sustainability of agriculture and the environment due to ongoing drought conditions. This study utilizes a model to estimate the risk of agricultural drought based on the principles of information diffusion theory. The goal is to assess the likelihood of agricultural drought occurring in the plains of Shazand, Khomein, and Saveh. By using agricultural data and meteorological information, three main aspects were taken into consideration: the susceptibility of environments prone to disasters, the ability to withstand risk, and the collective exposure to risk. To measure the risk of agricultural drought, three indicators were selected: the vulnerability of the area to drought, the percentage of abnormal rainfall, and the frequency of disaster occurrences. The results of the investigation revealed that the assessments of risk, when viewed from the perspective of disaster-prone environments, hazards, and the population exposed to risk, were significantly elevated. Concerning the susceptibility of disaster-prone environments, the examined plains show a concentrated range between severe and extremely severe sensitivities, with values ranging from 0.6 to 0.9. When considering the precariousness of the risk, these regions are facing a notably high level of vulnerability to drought. In evaluating the overall risk, the incidence of drought-related disasters in these areas exceeds 0.5 on the scale. Consequently, the associated probability of such risks materializing is estimated at intervals of approximately 4.8, 3.2, and 1.2 years for the Shazand, Khomein, and Saveh plains, respectively.

In line with the study of land use and land cover, remote sensing technology has been welcomed by many researchers as a source of spatial information production and suitable tools, which shows the accuracy and validity of these maps. The purpose of this research is to evaluate and compare the accuracy of preparing land use maps using two methods of remote sensing and one method of visual interpretation of Google Earth images in the Kasilian representative watershed. In this research, after taking educational samples using Google Earth software and implementing them on the Landsat 9 image of 2021, classification of images was done in ENVI software, and the land use map was prepared based on training samples, Neural Network and SVM methods. In the method of visual interpretation, all land uses in Google Earth images were manually digitized and a land use map was obtained. Then, the accuracy of the map was checked for all three methods and the results showed that the map obtained from visual interpretation of Google Earth images with overall accuracy and Kappa coefficient of 100% was in agreement with the ground reality compared to Neural Network and SVM methods with overall accuracies of 87.6% and 88.2% and Kappa coefficients of 76% and 77.8%, respectively. However, due to the time-consuming visual interpretation method, especially for large watersheds, and the acceptable accuracy of Neural Network and SVM methods, it is suggested to use advanced methods to prepare land use maps, especially in large watersheds.

Given the importance of the geometry of hillslopes in producing runoff and sediment and the influence of soil surface cover on these two components, the present study was conducted to investigate the effect of Vetiver vegetation cover and surface rock fragment cover on a convex-parallel hillslope. After preparing the plot based on the geometry of the convex-parallel hillslope and transferring Vetiver vegetation cover and surface rock fragment cover with 30% coverage, rainfall simulation was performed, and the results showed that in this type of hillslope, surface rock fragment cover reduced the average volume of runoff by 13.6% compared to the control and reduced soil loss by 33.37%. In addition, Vetiver vegetation cover led to a 65.76% reduction in runoff volume and a 69.4% reduction in soil loss. Furthermore, the statistical results showed that the volume of runoff and soil loss in the convex-parallel hillslope in protective treatments were significantly different from the control (Pvalue <0.05), indicating that the addition of surface rock fragment cover and Vetiver vegetation cover reduces the volume and soil loss. These results demonstrate the positive effect of both covers on reducing runoff and sediment. Therefore, depending on the goal and the conditions of the study area, appropriate cover can be used.

Large flood events occurrence such as the 18 March 2019 flood event in the city of Gonbad in Golestan Province has been often associated with socioeconomic and ecologic damages. The aim of this research is to simulate flood hydrograph using the HEC-HMS hydrological model and to evaluate model performance at the Arazkuseh Watershed outlet, located in the vicinity of Gonbad, and also for the upstream tributaries namely the Minodasht and the Nodeh Khandooz Watersheds. The HEC-HMS model was run by applying the SCS-Curve Number, the SCS-Unit Hydrograph, and the Muskingum-Cunge methods. The parameters of CN, initial abstraction, lag time, and the Manning's roughness coefficient were calibrated for the river gauge stations. The averaged calibrated parameters were used to validate the model at the gauge stations. The sensitivity analysis indicates that CN has the greatest influence on the model performance. The shapes of the simulated hydrographs in the validation stage show that the model underestimates the peak flows for the Arazkuseh Station. Whereas, the statistical indices of NSE, R2 and KGE for the validated hydrographs at the Arazkuseh Station were 0.81, 0.89 and 0.67, for the Nodeh Station were 0.85, 0.91 and 0.74, and for the Lazoreh Station identified as 0.62, 0.72 and 0.61, respectively. The analysis indicates the acceptable performance of the model. Considering the performance of the HEC-HMS model for the Arazkuseh Watershed and its upstream tributaries, the model can be used to predict the hydrological impacts of applying flood risk reduction scenarios in the upstream watersheds of Gonbad.

Traditional methods of irrigation and resource utilization lack an organized strategy for managing water resources. There are often inefficiencies in the way irrigation water is used in fields and plains, leading to irrigation waste and inefficient water utilization. Considering the size and dispersion of the land, the variety of crops, the time and location at which they are planted, and the distance between the source of water and the point where it is consumed, irrigation and drainage networks are essential for the best management of water resources. Therefore, in the present study, we have attempted to optimize the use of water in the agricultural sector by taking into account both the virtual water of plant cultivation and growth as well as the environmental conditions relevant to it, both geographically and temporally. Based on the calculations presented in this research and comparisons with the existing traditional method, the amount of water consumed in agriculture in the study area, which is part of Isfahan Province's southern part in the Gavkhoni lagoon, is estimated to be approximately 70 percent reduced if the solution presented in this study is implemented. It would be equivalent to using about 30% of the amount of water currently consumed and something equivalent to about 300 thousand cubic meters of water consumption reduction, which is a significant amount.

Drought is one of the destructive phenomena with adverse impacts on water resources and water needs. Machine-learning models are among the helpful tools in time-series prediction that can provide suitable results without the requirements for basic information about a system. In this study, adaptive neuro-fuzzy inference system (ANFIS) and least square support vector regression (LSSVR) models were utilized to predict the standardized precipitation index (SPI) as a meteorological drought indicator and streamflow drought index (SDI) as a hydrological drought indicator for a period (2001-2019). Ajabshir, located in the northwest of Iran, was selected as the study area, where the data of Qaleh Chay meteorological and hydrological stations were used to calculate SPI and SDI, respectively. The precipitation and flow rate data were considered input variables of the machine-learning models in predicting the SPI and SDI, respectively. The results revealed that during the period under review, meteorological drought was more severe in 2004-2011. While in this period, hydrological drought was more severe in 2007-2011 (SPI<-3). Moreover, the prediction results of the indices showed that the performance of the LSSVR model was better than that of ANFIS for both indicators. Using LSSVR, the RMSE and MAPE error evaluation criteria for SPI were 0.74 and 0.59, respectively, while these values for SDI were obtained as 0.62 and 0.46, respectively. The findings of this study show that machine-learning models are suitable tools for predicting drought indicators. Therefore, it is suggested to use such models in predicting drought indicators in other similar regions.

Accurate calculation of reference evapotranspiration is one of the basic tasks to achieve optimal management of water resources. In this research, the evapotranspiration of Bushehr reference was calculated by the Fau Penman Mantis method. Then, temperature methods (Hargreaves Samani and Blaney Cridle) and radiation methods (modified Mc King, Tork and Prestley Taylor) were also used to calculate evaporation transpiration. The results obtained from these methods were compared with the combined Fau Penman Mantis method. The results showed that among the two temperature methods, the Hargreaves Samani method and among the radiation methods, the Prestley Taylor method had closer results to the combined Fau Penman Mantis method. Artificial intelligence, support vector machine, random forest and cubist models were also used to estimate reference evaporation transpiration. The data used included minimum, maximum and average temperature, relative humidity, sunshine hour and wind speed during a thirty year statistical period from 1370 to 1400. In order to check the results of the mentioned models, the standard evaluation criteria of error mean square, absolute mean error and R2 explanation coefficient were used. The results showed that all three models were highly accurate in simulating evaporation transpiration. Cubist model with higher R2 (0.95), the lowest mean squared error (0.87) and the lowest absolute mean error (0.38) was chosen as the best method for evaporation transpiration.

Groundwater pollution is severely affected by different land uses, so that it is created widespread contamination in different areas in groundwater and endangers the health of human, livestock and other living organisms. In this study, the nitrate content of 55 water sources in Sirjan plain aquifer was investigated. Then, 5 types of land use were identified in Sirjan plain. Then, the polygon of Sirjan plain was mapped based on 5 types of land use to determine the role of each land use in the amount of groundwater nitrate in Sirjan plain. Then the amount of area and nitrate of different land uses were divided into 7 classes. The results showed that range land use in Sirjan plain has the highest level in aquifer and the lowest level of planting forests in Sirjan plain aquifer. Considering the minimum and maximum nitrate distance in Sirjan plain, the results showed that range land use has a lot of nitrate spatial variability compared to other land uses, and industrial land use has the least spatial variation in nitrate compared to other uses. In general, according to the mean amount of nitrate in Sirjan plain aquifer, the results showed that the minimum nitrate mean in different land uses of Sirjan plain is related to agricultural land use with 7.53 ppm and the maximum nitrate mean in different land uses of Sirjan plain is related to industrial land use with 10.24 ppm and the average nitrate level in planted forests, range and residential land uses with small changes is between agricultural and industrial land uses. In general, among all nitrate classes in Sirjan plain, the results showed that range land use has the highest percentage of land area, and industrial and planted forests land uses have the lowest percentage of land area.

Cultural heritage is the national wealth of the country, which does not belong only to the present generation, but must remain for future generations. Water supply has always been an important challenge for human societies. This challenge caused the local people to start building water storage and supply structures to supply their water needs, the pond was built as a solution to store water. The aim of the current research is to investigate the role of historical water structures in the sustainable development of tourism in Qeshm Island. In this research, using conventional or conventional qualitative content analysis method floors, sub-floors and sub-floors, the role of historical water storage and transfer structures in the sustainable development of tourism was explained with an emphasis on Qeshm Island. In the data collection process, techniques such as participatory observation, in-depth and unstructured interviews, and argumentative document studies were used. After analyzing the research data, focusing on the analysis of the factors affecting the role of the historical water structures of the pond in the sustainable development of Qeshm Island tourism, 3 floors, 8 sub-floors and 38 sub-floors were identified and explained. The classes include: "Economic Sustainability", "Social-Cultural Sustainability", and "Ecological (Environmental) Sustainability" and finally the main classes are explained based on the three principles of sustainable development and finally identified components and the role of historical structures. Water storage and water transfer were emphasized in the sustainable development of tourism.

Increasing global awareness of the problem of climate change and its potential threats, such as internal conflicts, border disputes, migration, energy security and other scarce resources, social crises and humanitarian crises on the one hand, and the change and transformation of the concept of international security and conceptual transfer From the global dimension to the individual dimension after the end of the Cold War, on the other hand, it has led to a direct connection between climate change and human security. On the other hand, the interrelationship of people's security in different parts of the world and the necessity of collective action to guarantee it has led to the transformation of human security as a common concern in the face of important threats such as climate change that have a cross-border nature. In this article, we have tried to analyze the relationship between climate change and human security with the descriptive-analytical method, while examining the regular basis of climate change prevention and concepts related to human security, and we come to the conclusion that the effects of water changes and air is not limited to environmental categories and has brought forward the connection of these changes with international peace and security and as a result human security, which requires the intervention and confrontation of the international law system, especially the International Law Commission, whose task is to develop And it is responsible for compiling international law, it reveals.