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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.

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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.

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Proposing a rigorous empirical model for estimating the bubble point pressure in heterogeneous carbonate reservoirs.