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This study develops the Multiobjective Grey Wolf Optimization (MOGWO) algorithm to obtain the optimum rules on the operation of the Golestan Dam in Golestan Province, Iran, under the climate change conditions. The tow objective functions defined in the optimization process include minimizing the vulnerability and maximizing the reliability indices of the model under baseline and climate change conditions periods. Results showed that the river flow would decline by 0.17 percent of the baseline period under climate change conditions in addition to increasing the temperature by 20%, as well as decreasing the rainfall by 21.1%. Moreover, the extent of vulnerability index variations in baseline and climate change conditions was 16–45% and 10–43%, respectively. The range of reliability index variations in baseline and climate change conditions was 47–90% and 27–93%. On the other hand, the vulnerability index has also been measured at 29% and 27% for baseline and climate change conditions, respectively, with 75 percent of reliability. Comparison of the reservoir release rate and water demands for all of the Pareto points indicates a rise in release rates for climate change conditions relative to the baseline one; as the result, the higher adjustment in the reservoir release rates to its demand volumes will be highlighted as the higher dam efficiency in climate change conditions.

One of the management tools for sediment and erosion control in the different scales from plot to watershed is informing about soil displacement process that can be obtained using fallout radionuclide spectroscopy. In recent decades, use of the radionuclides for determining sedimentation rate was common, among which Cesium ( 137 Cs) is the most often used. In this research, three, 4-meter long sediment cores were collected from the western part of the Anzali Lagoon. The Anzali Lagoon is one of the sediment treated ecosystems in the north of Iran. The level of 137 Cs of the sediment samples was measured based on Spectrometry analysis in the Atomic Energy Organization of Iran. The grain size distribution showed that the sediment samples were mainly fine textured (Silt with low plasticity properties). The results represented that the highest amount of the 137 Cs was observed in the depth of 2.4-2.7 m, which can be related to the Chernobyl disaster in 1986. An overall sedimentation rate of 8.5 cm yr -1 (=119 kg m -2 yr -1 ) was obtained based on the 137 Cs calendar of the sediment cores. This sedimentation rate is considerable, and a special arrangement is necessary to save the Lagoon.

Managing water resources requires the optimum operation of dam reservoirs. To satisfy the downstream water demand in the operational optimization of Boostan dam reservoir, the improved whale optimization algorithm (IWOA) performance was compared in the present study with that of its constituents (i.e., the whale optimization and differential evolution) based on GAMS nonlinear programming results. The model evaluative indicators and an objective function were used to select the optimal algorithm. The findings suggested that IWOA resulted in the lowest computational duration and fastest convergence rate compared to the other algorithms. Additionally, the average water demand and discharge volume of IWOA were 3.21 × 106 m3 and 3.03 × 106 m3, respectively. In contrast, the other algorithms yielded lower water release volumes. IWOA enhanced the WOA performance by 21.7% through reducing the variation coefficient by 78% in optimizing the objective function. The water demand was therefore more effectively satisfied by the IWOA compared to the other algorithms. Furthermore, the IWOA resulted in a lower amount of errors. The hybrid algorithm performance increased in terms of all the evaluative indicators. Developing multicriteria decision-making models using TOPSIS and the Shannon entropy also suggested the IWOA excels the other algorithms in optimizing the reservoir operational problem.

Recently, global warming problems with rapid population growth and socio-economic development have intensified the demand for water and increased tensions on water supplies. This research evolves the Multi-Objective Coronavirus Optimization Algorithm (MOCVOA) to obtain operational optimum rules of Voshmgir Dam reservoir under the climate change conditions. The climatic variables downscaled and predicted by the Bias Correction Spatial Disaggregation (BCSD) method of MIROC-ESM model, was introduced into the ExtremeLearning Machine (ELM) modelto evaluate the future runoff flowing into the reservoir. The model objective functions included minimizing vulnerability and enhancing reliability indices during baseline and climate change periods. Results revealed that under climate change conditions, the river flow would decrease by 0.17%, increase the temperature up to 2°C and decrease the rainfall by 23.8%, corresponding to the baseline period. Moreover, the extent of vulnerability index variations in the baseline and climate change conditions were also determined as 20-38% and 13-40%, respectively. The reliability index changes under the baseline and climate change conditions obtained were, 57-85% and 40-91%. Therefore, the vulnerability index was also measured at 33% and 30% for baseline and climate change conditions, respectively, with 80% of reliability index. Finally, the comparison of reservoir performance in meeting the water needs of downstream lands at the Pareto point of 80% reliability under both conditions indicated that the reservoir release rate would be more in line with the demand in the climate change conditions.

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