Snowmelt is an important source of stream flows in mountainous areas. This study investigated the impact of snowmelt on flooding. First, the study area was divided into four zones based on elevation. Second, the Snow-Covered Area (SCA) from 2013 to 2018 was estimated from daily MODIS images with the help of Google Earth Engine. Runoff in the area was then simulated using the Snowmelt Runoff Model (SRM). As a result, short periods with high runoff and the possibility of floods were identified, while the contribution of snowmelt and rainfall in the total runoff was separated. The results showed that while the snowmelt on average accounted for only 23% of total runoff in the zone with elevation under 2000 m, the ratio increased with elevation, ultimately reaching as high as 87% in the zone with elevation above 3000 m. As the height increases, the effect of snow on runoff and flooding increases so much that it should not be ignored. However, in most hydrological studies, the effect of snow is ignored due to the lack of sufficient data about snow. This study showed that snow can be very effective, especially in high areas.

Climate change affects hydroclimatic variables, and assessing the uncertainty in future predictions is crucial. This study aims to explore variations in temperature and precipitation in the Kerman Plain under climate change impacts between 2023 and 2054. For this purpose, two climate models, MRI-ESM-2 and BCC-CSM2-MR, were used to simulate precipitation and temperature under two different scenarios. The Mann–Kendall test was employed to analyze the annual time series in the future period. The results indicated an increase in the average temperature of about 1.5 degrees Celsius based on both scenarios in the coming years. Furthermore, an average annual increase of 6.37 mm of precipitation was predicted under the SSP585 scenario. Meanwhile, under the SSP585 scenario, an increase was estimated using the MRI-ESM-2 model, and a decrease was predicted with the BCC-CSM2-MR model. The Mann–Kendall test revealed a downward trend in the BCC-CSM2-MR model under both scenarios and an upward trend in the MRI-ESM-2 model under both scenarios. The bootstrap method and the R-factor index were exploited in this study with a 95% confidence interval to estimate the uncertainty of the predicted data. The results demonstrated that the predicted precipitation is more uncertain than the temperature. Finally, it is postulated that the obtained results provide necessary information for water resource management under a changing climate in the study area.

Planning for the future of water and energy supply systems in urban areas requires a thorough assessment of associated risks. In this study, monthly water and energy demand data from 2011 to 2022 in an arid city was used to predict the corresponding demands from 2023 to 2032 using the seasonal auto-regressive integrated moving average (SARIMA) method. The aim is to estimate future water and energy supply risks both individually and jointly, using cumulative distribution functions (CDFs) derived from historical data. The main focus is to calculate the combined risk of water and energy, referred to as the water–energy nexus (WEN) risk. Based on the interdependent relationship between water and energy, the Copula function was utilized to model the bivariate distribution between these two variables. Pearson correlation analysis indicated a strong correlation between water and energy supplies. Among the distributions fitted to the data, the log-normal and gamma distributions were the best fit for water supply and energy supply systems, respectively, with the lowest Akaike information criterion (AIC) values. The Gumbel Copula, with a parameter of 1.66, was identified as the most suitable for modeling the joint distribution, yielding the lowest AIC value. The results indicate that the risks associated with energy supply, water supply, and their joint dependency could exceed 0.8% in the future, highlighting a potentially critical situation for the city. The trend analysis revealed that forecasted water and energy demands and their corresponding risks and the WEN risk are expected to have a significant upward trend in the future. Finally, local authorities need to explore alternative sources to supply water and energy in the future to address the ever-growing water and energy demands.