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Changes in land cover (LC) can alter the basin hydrology by affecting the evaporation, infiltration, and surface and subsurface flow processes, and ultimately affect river water quantity and quality. This study aimed to monitor and predict the LC composition of a major, transboundary basin contributing to the Caspian Sea, the Aras River Basin (ARB). To this end, four LC maps of ARB corresponding to the years 1984, 2000, 2010, and 2017 were generated using Landsat satellite imagery from Armenia and the Nakhchivan Autonomous Republic. The LC gains and losses, net changes, exchanges, and the spatial trend of changes over 33 years (1984–2017) were investigated. The most important drivers of these changes and the most accurate LC transformation scenarios were identified, and a land change modeler (LCM) was applied to predict the LC change for the years 2027 and 2037. Validation results showed that LCM, with a Kappa index higher than 81%, is appropriate for predicting LC changes in the study area. The LC changes observed in the past indicate significant anthropogenic impacts on the basin, mainly by constructing new reservoir dams and expanding agriculture and urban areas, which are the major water-consuming sectors. Results show that over the past 33 years, agricultural areas have grown by more than 57% from 1984 to 2017 in the study area. Results also indicate that the given similar anthropogenic activities will keep on continuing in the ARB, and agricultural areas will increase by 2% from 2017 to 2027, and by another 1% from 2027 to 2037. Results of this study can support transboundary decision-making processes to analyze potential adverse impacts following past policies with neighboring countries that share the same water resources.

In recent years, remote sensing technology has enabled researchers to fill the existing statistics and research gaps on evapotranspiration in different land use classes. Thus, a remotely sensed-based approach was employed to investigate how evapotranspiration rates changed in different land use/cover classes across the Lake Urmia Basin from 2016 to 2020. This was accomplished by applying the Surface Energy Balance System (SEBS) and the maximum likelihood algorithm. Results showed that from 2016 to 2020, grassland, savanna, and wetland decreased by 1%, 0.58%, and 1%, respectively, whereas an increase of 0.4%, 0.4%, 2.5%, and 1.2% occurred in cropland, urban, shrubland, and water bodies, respectively. Based on the model’s results, over 98, 63, 90, 93, and 91% of the studied area, respectively, experienced a value of evapotranspiration between 0–6, 3–8, 0–4, 0–4, and 0–6 mm from 2016 to 2020. It was also found that these values are more closely related to water bodies and wetlands, followed by cropland, urban areas, savanna, non-vegetated, grassland, and shrubland. A strong correlation with R2 > 70% was observed between the SEBS and the ground-measured values, while this value is lower than 50% for the MODIS Global Evapotranspiration Project (MOD16A2). The findings suggest that evapotranspiration and land use/cover can be extracted on a large-scale using SEBS and satellite images; thus, their maps can be presented in an accurate manner.

Regarding the ever increasing issue of water scarcity in different countries, the current study plans to apply support vector machine (SVM), random forest (RF), and genetic algorithm optimized random forest (RFGA) methods to assess groundwater potential by spring locations. To this end, 14 effective variables including DEM-derived, river-based, fault-based, land use, and lithology factors were provided. Of 842 spring locations found, 70% (589) were implemented for model training, and the rest of them were used to evaluate the models. The mentioned models were run and groundwater potential maps (GPMs) were produced. At last, receiver operating characteristics (ROC) curve was plotted to evaluate the efficiency of the methods. The results of the current study denoted that RFGA, and RF methods had better efficacy than different kernels of SVM model. Area under curve (AUC) of ROC value for RF and RFGA was estimated as 84.6, and 85.6%, respectively. AUC of ROC was computed as SVM- linear (78.6%), SVM-polynomial (76.8%), SVM-sigmoid (77.1%), and SVM- radial based function (77%). Furthermore, the results represented higher importance of altitude, TWI, and slope angle in groundwater assessment. The methodology created in the current study could be transferred to other places with water scarcity issues for groundwater potential assessment and management.

The decline in Urmia Lake basin’s water resources has resulted in a severe drought of the lake. The drought of this hyper-saline lake has put lives of 6.4 million inhabitants at risk. This study was conducted to assess the technical and economic employability of a payment for ecosystem services (PES) method as a policy tool to improve water resources management of Siminehroud river basin which is the most important tributary of Urmia Lake basin. For this purpose, the target areas were identified after the development of a land-use map for the basin. Then, by recruiting the integrated interview method and distributing 398 questionnaires, the required data were collected to assess the employability of the proposed PES method. Among various PES schemes, two methods including a) payment for shifting irrigation methods and b) payment to change cropping patterns in the frame of “Willingness to Accept” (WTA) were proposed to farmers. The results suggest that farmers highly welcomed both proposed methods. The benefit–cost ratio (BCR) for the change in irrigation system was 3.98, whereas the changes for the cropping pattern were 0.8 (for rapeseed), 0.72 (for soybean), and 1.09 (for safflower). As a result, shifting irrigation methods and changing cultivation patterns to safflower are both economically justifiable.

Scenarios for monitoring land cover on a large scale, involving large volumes of data, are becoming more prevalent in remote sensing applications. The accuracy of algorithms is important for environmental monitoring and assessments. Because they performed equally well throughout the various research regions and required little human involvement during the categorization process, they appear to be resilient and accurate for automated, big area change monitoring. Malekshahi City is one of the important and at the same time critical areas in terms of land use change and forest area reduction in Ilam Province. Therefore, this study aimed to compare the accuracy of nine different methods for identifying land use types in Malekshahi City located in Western Iran. Results revealed that the artificial neural network (ANN) algorithm with back-propagation algorithms could reach the highest accuracy and efficiency among the other methods with kappa coefficient and overall accuracy of approximately 0.94 and 96.5, respectively. Then, with an overall accuracy of about 91.35 and 90.0, respectively, the methods of Mahalanobis distance (MD) and minimum distance to mean (MDM) were introduced as the next priority to categorize land use. Further investigation of the classified land use showed that good results can be provided about the area of the land use classes of the region by applying the ANN algorithm due to high accuracy. According to those results, it can be concluded that this method is the best algorithm to extract land use maps in Malekshahi City because of high accuracy.

This study analyzed the potential use of Payment for Ecosystem Services (PES) as a strategy for improving water supply management. This study focused on the Siminehroud Sub-basin due to its high importance to the Basin of Urmia Lake (UL). Siminehroud is the second provider of water (by volume) to Urmia Lake. To evaluate the technical and economic feasibility of a PES scheme, the current land use map was extracted using satellite imagery. In addition, the two algorithms of Support Vector Machines (SVMs) and Maximum Likelihood (ML) are used for Landsat images classification, rather than analyzing the relationship between land use and ecosystem services. Then, the most relevant ecosystem services provided in the region were evaluated using the Benefit Transfer Method. In the last step, by designing and implementing a survey, on the one hand, the local farmers’ Willingness to Accept (WTA) cash payments for reducing the area they cultivate, and on the other hand, the farmers’ Willingness to Pay (WTP) for managing the water consumption were determined. The results illustrated that the WTA program is more acceptable among the beneficiaries. It is also notable that this program needs very high governmental funding. Furthermore, the results of the program indicate that the land area out of the cultivation cycle will gradually increase while the price of agricultural water will also increase.

Floods are natural hazards with serious impact on many aspects of human life. The Intergovernmental Panel on Climate Change (IPCC) reported that climate change already has significant impact on magnitude and frequency of flood events worldwide. Thus, it is suggested to adopt strategies to manage damage impacts of climate change. For this, involving the local community in the decision-making process, as well as experts and decision-makers, is essential. We focused on assessing the social acceptability of flood management strategies under climate change through a socio-hydrological approach using the Contingent Valuation Method (CVM). For this purpose as well, hydro-climate modelling and the Analytical Network Process (ANP) were used. Among twelve investigated flood management strategies, “river restoration”, “agricultural management and planning”, and “watershed management” were the publicly most accepted strategies. Assessment of the social acceptability of these three strategies was carried out by use of the CVM and Willingness to Pay (WTP) methodology. Generally, 50%, 38%, and 18% were willing to pay and 44%, 48%, and 52% were willing to contribute flood management strategy in zones 1, 2, and 3, respectively. Overall, peoples’ WTP for flood management strategies decreased with increasing distance from the river. Among different investigated dependent variables, household income had the highest influence on WTP.

Nowadays, major attention is being paid to curing different types of cancers and is focused on natural resources, including oceans and marine environments. Jellyfish are marine animals with the ability to utilize their venom in order to both feed and defend. Prior studies have displayed the anticancer capabilities of various jellyfish. Hence, we examined the anticancer features of the venom of Cassiopea andromeda and Catostylus mosaicus in an in vitro situation against the human pulmonary adenocarcinoma (A549) cancer cell line. The MTT assay demonstrated that both mentioned venoms have anti-tumoral ability in a dose-dependent manner. Western blot analysis proved that both venoms can increase some pro-apoptotic factors and reduce some anti-apoptotic molecules that lead to the inducing of apoptosis in A549 cells. GC/MS analysis demonstrated some compounds with biological effects, including anti-inflammatory, antioxidant and anti-cancer activities. Molecular docking and molecular dynamic showed the best position of each biologically active component on the different death receptors, which are involved in the process of apoptosis in A549 cells. Ultimately, this study has proven that both venoms of C. andromeda and C. mosaicus have the capability to suppress A549 cells in an in vitro condition and they might be utilized in order to design and develop brand new anticancer agents in the near future.