Optimal Water Allocation of Surface and Ground Water Resources Under Climate Change with WEAP and IWOA Modeling

Water Resources Management - The water evaluation and planning (WEAP) approach and the invasive weed optimization algorithm (IWOA) are herein employed to determine the optimal operating policies in...     

Multi-criteria Decision-making Approach for Environmental Impact Assessment to Reduce the Adverse Effects Of Dams

Water Resources Management - Environmental Impact Assessments (EIAs) of development projects are necessary to minimize negative impacts and maximize benefits. The objective of this paper is to...     

Selecting the Best Approach to Modeling the Performance of Water Supply System Using the Combination of Rough Set Theory with Multi-Criteria Decision Making

The purpose of this study is to select the best modeling approach (simulation or optimization) for operation the water supply system using multi-criteria decision-making method. For this purpose, the Geophysical Fluid Dynamics Laboratory-Earth System Models (GFDL-ESM2M) and the Model for Interdisciplinary Research on Climate-ESM (MIROC-ESM) models were selected to predict the changing trend of the climatic variables of rainfall and temperature, respectively. Then Artificial Neural Network (ANN) model and a decision support system tool named Cropwat were used to simulate water resources and consumption; and to model the behavior of the water supply system, the MODified SYMyld (MODSIM) (as simulator) and the modeling language and optimizer LINGO 18 (as optimizer) were used in the future time period (2026–2039) and the results were compared with the baseline period (1987–2000) for the Idoghmush reservoir (Iran). The results of MODSIM simulation model show that the indexes of reliability, vulnerability, reseiliency and flexibility in the future time period under the RCP2.6 emission scenario compared to the baseline time period decreased by 9%, decreased by 22%, increased by 4%, and decreased by 2%, respectively. The results of the LINGO 18 optimization model show that the reliability, vulnerability, resiliency and flexibility indexes in the future time period under the RCP2.6 emission scenario compared to the baseline time period decreased by 13%, decreased by 17%, increased by 14% and increased by 3%, respectively. Due to the different results obtained from optimization and simulation approaches for the study area, the Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA) multi-criteria decision-making method was used to select a more appropriate approach. The results show that for water resources management planning, the simulation approach is given priority over the optimization approach due to its characteristics.

     

Investigation of the creep behavior of graphene oxide nanoplatelet-reinforced adhesively bonded joints

In this paper, the effect of adding graphene oxide nano-platelets (GONPs) into the adhesive layer was investigated on the creep behavior of adhesively bonded joints. The neat and GONP-reinforced adhesive joints were manufactured and tested under creep loading with different stress and temperature levels. 0.1?wt% GONPs revealed the highest improvement on the adhesive joint creep behavior amongst the studied weight percentages. Furthermore, the effect of GONPs on the creep behavior of adhesive joints was more significant at higher temperatures. It was found that adding 0.1?wt% of GONPs into the adhesive layer imposed reductions of 21%, 31% and 34% in the elastic shear strains and reductions of 24%, 31% and 37% in the creep shear strains of SLJs under testing temperatures of 30, 40 and 50?°C, respectively. The Burgers rheological model was employed for simulating the creep behavior of the neat and GONP-reinforced adhesive joints. The Burgers model parameters were obtained as functions of testing temperature, creep shear stress and GONP weight percentage using a response surface methodology. Reasonable agreement was obtained between the modeled and experimental creep behaviors of the adhesive joints.     

Scenario Assessment of Streamflow Simulation and its Transition Probability in Future Periods Under Climate Change

The effect of climate change on water resources is an important challenge. To analyze the negative effects of this phenomenon and recommend adaptive measures, it is necessary to assess streamflow simulation scenarios and streamflow transition probabilities in future periods. This paper employs the HadCM3 (Hadley Centre Coupled Model, version 3) model to generate climate change scenarios in future periods (2010–2039, 2040–2069, and 2070–2099) and under A2 emission scenarios. By introducing climatic variable time series in future periods to the IHACRES (Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data) hydrological model, long-term streamflow simulation scenarios are produced. By fitting statistically different distributions on runoff produced by using goodness-of-fit tests, the most appropriate statistical distribution for each month is chosen and relevant statistical parameters are extracted and compared with statistical parameters of runoff in the base period. Results show that long-term annual runoff average in the three future periods compared to the period 2000–1971 will decrease 22, 11, and 65 %, respectively. ?Despite the reduction in total runoff volume in future periods compared to the baseline period, the decrease is related to medium and high flows. In low flows, total runoff volumes for future periods compared to the baseline period will increase 47, 41, and 14 %, respectively. To further assess the impact of annual average runoff on flows, it is necessary to examine the correlation of time series using streamflow transition probabilities. To compare the streamflow transition probability in each of the future periods with base period streamflow in each month, streamflow is discretized and performance criteria are used. Results show a low coefficient of correlation and high error indicators.     

Application of the Grasshopper Optimization Algorithm (GOA) to the Optimal Operation of Hydropower Reservoir Systems Under Climate Change

Hydropower is a low-carbon energy source, which may be adversely impacted by climate change. This work applies the Grasshopper Optimization Algorithm (GOA) to optimize hydropower multi-reservoir systems. Performance of GOA is compared with that of particle swarm optimization (PSO). GOA is applied to hydropower, three-reservoir system (Seymareh, Sazbon, and Karkheh), located in the Karkheh basin (Iran) for baseline period 1976–2005 and two future periods (2040–2069) and (2070–2099) under greenhouse gases pathway scenarios RCP2.6, RCP4.5, and RCP8.5. GOA minimizes the shortage of hydropower energy generation. Results from GOA optimization of Seymareh reservoir show that average objective function in baseline is 85 and minimum value of average objective function in 2040–2069 would be under RCP2.6 (equal to 0.278). Optimization of Seymareh-reservoir based on PSO shows that average value of objective function in baseline is less (that is, better) than value obtained with GOA (10.953). Optimization results for two-reservoir system (Sazbon and Karkheh) based on GOA optimization show that objective function in baseline is 5.44 times corresponding value obtained with PSO, standard deviation is 2.3 times that calculated with PSO, and run-time is 1.5 times PSO’s. Concerning three-reservoir systems it was determined that objective function based on PSO had the best value (the lowest energy deficit), especially in future. GOA converges close to the best objective function, especially in future-periods optimization, and convergence to solutions is more stable than PSO’s. A comparison of performance of GOA and PSO indicates PSO converges faster to optimal solution, and produces better objective function than GOA.

     

Multi-Objective Firefly Integration with the K-Nearest Neighbor to Reduce Simulation Model Calls to Accelerate the Optimal Operation of Multi-Objective Reservoirs

Reservoirs are used as one of the surface water sources for different and often conflicting water supply purposes. Given the complex management policies governing a basin, it is essential to simultaneously consider different goals and cope with the associated trade-off in water resources management. This purpose requires coupling a multi-objective optimization algorithm with a reservoir simulation model, which this approach increases required computational efforts. Various simulation–optimization approaches have been developed and used for solving the related problems. However, they often have complicated methods and certain limitations in real-world applications. In this study, a new multi-objective firefly algorithm—K nearest neighbor (MOFA-KNN) hybrid algorithm is developed which is time-efficient and is not as complicated as previous approaches. The proposed algorithm was evaluated for both benchmark and real problems. The results of the benchmark problem showed that the execution time of the MOFA-KNN hybrid algorithm was up to 99.98% less than that of the multi-objective firefly algorithm (MOFA). In the real problem, the MOFA-KNN algorithm was linked to the 2D hydrodynamic and water quality model, CE-QUAL-W2, to test the developed framework for reservoir operation. The Aidoghmoush reservoir as a case study investigated to minimize the total released dissolved solids (TDS) and the water temperature difference between the inflow and the outflow. The results demonstrated that the MOFA-KNN algorithm significantly reduced the simulation–optimization execution time (>?660 times compared with MOFA). The minimum released TDS from the reservoir was 13.6 mg /l and the minimum temperature difference was 0.005 °C.

     

Reservoir Water-Quality Projections under Climate-Change Conditions

Water Resources Management - Reservoirs are key components of water infrastructure that serve many functions (water supply, hydropower generation, flood control, recreation, ecosystem services,...     

Assessment of Water Resources Development Projects under Conditions of Climate Change Using Efficiency Indexes (EIs)

The purpose of this study is to evaluate Gharanghu multi-purpose reservoir system (East Azerbaijan, Iran) using efficiency indexes (EIs) affected by climate change. At first, the effects of climate change on inflow to the reservoir, as well as changes in the demand volume over a time interval of 30 years (2040–2069) are reviewed. Simulation results show that inflow to the reservoir is decreased in climate change interval compared to the baseline interval (1971–2000), so that comparison of long-term average monthly inflow to the reservoir in climate change interval is reduced about 25% compared to the baseline. Also, water demand in climate change interval will increase, namely volume of water demand for agricultural, drinking and industrial, and environmental in climate change interval is expected to increase by 20%. The simulation results of the water evaluation and planning (WEAP) model is used to determine EIs of multi-purpose reservoir system. Next, three scenarios of water supply for climate change interval are introduced to WEAP model, keeping variable of parameter related to water demand volume (based on different percentages of supply) and keeping constant of the parameter related to the volume of inflow to the reservoir. Results show that system EIs in climate change interval will have a disadvantage compared to the baseline. So that, reliability, vulnerability, resiliency and flexibility indexes in climate change interval based on 100% of water supply compared to the baseline will decrease 18%, increase 150%, decrease 33%, and decrease 47%, respectively. These indexes based on 85% of supply compared to the baseline will decrease 12%, increase 75%, decrease 30%, and decrease 39%, respectively. Also, those based on 70% of supply compared to the baseline will decrease 1%, will be without change, decrease 18%, and decrease 18%, respectively. Changes in indexes in future interval indicate the need to manage water resource development projects in the basin.     

Ultrafiltration using the Amicon MPS-1 for assessing methadone plasma protein binding

Steroid Receptor Coactivator-1 (SRC-1) interacts with nuclear receptors only when they are bound to the ligands and enhance the transactivation. We identified splicing variants encoding three isoforms, SRC-1, SRC-1(-Q), and SRC-1E, generated by alternative usage of an exon(s) and splicing acceptor sites. RT-PCR analysis showed that SRC-1E was more abundantly expressed than SRC-1 or SRC-1(-Q) at the mRNA level in all the cell lines tested. SRC-1E lacks 56 amino acids of SRC-1 and has unique 14 amino acids at the carboxyl terminus, while SRC-1(-Q) differs from SRC-1 by deletion of only one glutamine residue. Since the C-terminal domain of SRC-1 has been shown to be involved in the interaction with nuclear receptors, the enhancement of transactivation by these three isoforms was tested. SRC-1E enhanced thyroid hormone dependent transactivation of reporter gene expression more profoundly than SRC-1 or SRC-1(-Q). Taken together, it was suggested that SRC-1E is the major isoform of SRC-1 to mediate thyroid hormone action.