نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران
2 گروه مهندسی عمران، دانشگاه قم
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Extended Abstract
Background and Objective
This study focuses on optimizing the Zayandehrud Dam reservoir using two evolutionary algorithms—Grey Wolf Algorithm (GWA) and Particle Swarm Optimization (PSO)—to maximize reliability and minimize vulnerability in water resource management. Climate data, including temperature and precipitation, are extracted for the baseline period (1981–2010) and future intervals (2040–2069 and 2070–2099) using outputs from the HadCM3 model under two emission scenarios, A2 and B2. The impacts of climate change on water resources are assessed using the Artificial Neural Network (ANN) and IHACRES models. Additionally, the CROPWAT model analyzes climate change effects on water consumption and estimates irrigation demands.
Methodology
This study presents a comprehensive approach to optimizing the Zayandehrud Dam reservoir using single-objective evolutionary algorithms—particle Swarm Optimization (PSO) and the Grey Wolf Algorithm (GWA). The research focuses on maximizing reliability and minimizing vulnerability in reservoir operations while accounting for climate change effects. The study area, the Zayandehrud River Basin, is the first defined basin in the Central Plateau of Iran.
Climate scenarios for temperature and precipitation are extracted from the HadCM3 model for the baseline period (1981–2010) and future intervals (2040–2069 and 2070–2099) under two emission scenarios, A2 and B2. Using the spatial coordinates of the meteorological station under investigation, the time series of temperature and precipitation variables corresponding to the HadCM3 computational grid cell are retrieved.
To downscale climate variables, temperature and precipitation changes in future periods are calculated relative to the baseline and applied to observed data. Future climate variables are estimated by adding temperature scenarios to observed temperature values and multiplying precipitation scenarios with observed precipitation records.
Future runoff simulations are conducted using the IHACRES and ANN models. The estimation of water demand and consumption volumes is performed using the CROPWAT model. Subsequently, reservoir optimization is carried out utilizing PSO and GWA, with reliability maximization and vulnerability minimization considered as objective functions.
Findings
This study investigates the effects of climate change on rainfall, temperature, runoff, and water demand under A2 and B2 scenarios. Results indicate a notable temperature increase and reduced rainfall in the 2070-2099 compared to 2040-2069. Temperature and precipitation variations in the B2 scenario show a more significant temperature rise and precipitation reduction in the 2070-2099 than in 2040-2069.
Runoff simulation results demonstrate that the Artificial Neural Network (ANN) outperforms IHACRES, leading to its adoption for future flow predictions. The simulated monthly long-term average runoff using the ANN model in the A2 scenario decreased by 15.7% and 20.9% in the 2040-2069 and 2070-2099 periods, respectively. In the B2 scenario, the reductions were 17.2% and 26.5% compared to the baseline period.
Findings from the CROPWAT model indicate a rise in long-term average monthly water demand in future periods. Under scenario A2, demand increased by 100% and 170% in 2040-2069 and 2070-2099, respectively. Under scenario B2, demand increased by 94% and 121% compared to the baseline period. Water demand growth is more pronounced in the A2 scenario and in the 2070-2099 period compared to 2040-2069.
The time series analysis of demand and release optimization using GWA and PSO shows the system's attempt to maximize supply and reduce vulnerability, thereby minimizing failure intensity. The results indicate that GWA performs better than PSO in achieving this objective.
Conclusion
This study examines the impact of climate change on water resources and consumption in the Zayandehrud Basin, focusing on future periods. Additionally, optimal reservoir operation strategies for the Zayandehrud Dam have been assessed. The findings indicate:
Runoff under the A2 scenario will decrease by 15.7% in 2069–2040 and 20.9% in 2099–2070, while the B2 scenario will see reductions of 17.2% and 26.5%, respectively. Water demand will rise significantly, with an increase of 100% in 2069–2040 and 170% in 2099–2070 under A2, whereas B2 will exhibit a 94% increase in 2069–2040 and 121% in 2099–2070. Under baseline conditions, system reliability was 99.81% for GWA and 99.72% for PSO, while vulnerability was 0.53% for GWA and 0.71% for PSO. In the A2 scenario (2069–2040), reliability values for GWA and PSO were 89% and 88%, with corresponding vulnerability values of 9.8% and 10.1%. In A2 (2099–2070), reliability values for GWA and PSO declined to 80% and 79%, while vulnerability rose to 12.8% and 13.3%, respectively. In B2 (2069–2040), reliability values for GWA and PSO were 89% and 88%, while vulnerability was 10.4% and 10.7%, respectively. In B2 (2099–2070), reliability values dropped to 76% for GWA and 73% for PSO, with vulnerability increasing to 18.9% and 19.4%, respectively. Comparing both models reveals that GWA outperforms PSO by achieving higher reliability and lower vulnerability in reservoir operation.
کلیدواژهها [English]
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