Hydrological Drought Assessment in Northwestern Iran Based on Streamflow Drought Index (SDI)

Drought is a natural and worldwide phenomenon that occurs when water availability is significantly below normal levels during a significant period of time and cannot meet demand. This work focused on the hydrologic drought defined by the streamflow drought index (SDI) for overlapping periods of 3, 6, 9 and 12 months at 14 hydrometric stations in the northwest of Iran over the period 1975–2009. It was found that some of the streamflow volume series did not follow the normal distribution. The ability of the log-normal, exponential and uniform probability distributions was examined in order to choose the most suitable distribution, and the log-normal distribution was used to fit the long-term streamflow data. The results of the hydrological drought analysis based on the SDI showed that almost all the stations suffered from extreme droughts during the study period. Additionally, extreme droughts occurred most frequently in the last 12 years from 1997–1998 to 2008–2009.     

Groundwater Model Calibration by Meta-Heuristic Algorithms

Groundwater models are computer models that simulate or predict aquifer conditions by using input data sets and hydraulic parameters. Commonly, hydraulic parameters are extracted by calibration, using observed and simulated aquifer conditions. The accuracy of calibration affects other modeling processes, especially the hydraulic head simulation. Meta-heuristic algorithms are good candidates to determine optimal/near-optimal parameters in groundwater models. In this paper, two meta-heuristic algorithms: (1) particle swarm optimization (PSO) and (2) pattern search (PS) are applied and compared in the Ghaen aquifer, by considering the sum of the squared deviation (SSD) between observed and simulated hydraulic heads and the sum of the absolute value of deviation (SAD) between observed and simulated hydraulic heads as the objective functions. Results show that obtained values of the objective function are enhanced significantly by using the PS algorithm. Accordingly, PS improves (decreases) the SSD and SAD by 0.20 and 2.36 percent, respectively, compared to results reported by using the PSO algorithm. Results also indicate that the proposed PS optimization tool is effective in the calibration of aquifer parameters.     

Optimal Design of Concrete Canal Section for Minimizing Costs of Water Loss,Lining and Earthworks

Canal section design with minimum cost, which can be considered as an objective function, involves minimization of total costs per unit length of the canal, including direct costs of per cubic meter earthworks and per meter canal lining and indirect costs of water losses through canal seepage and evaporation. Since the costs (both direct and indirect) are associated with the canal geometry and dimensions, it is possible to lower them by optimization of the mentioned objective function. For this purpose, some constraints were subjected and considered to solve the problem. Flow discharge, as the main constraint, was considered in addition to the minimum permissible velocity and Froude’s number, as subsidiary constraints. MATLAB programming software was used to demonstrate and run the optimization algorithm. The results finally were illustrated in forms of dimensionless graphs, which simplify the optimum design of canal dimensions with minimum cost per meter length. Comparing the results with other similar studies, however show the importance and role of earthworks and lining costs, as well as including the subsidiary constraints in the optimization process.     

Identification of Trend in Reference Evapotranspiration Series with Serial Dependence in Iran

Monitoring the temporal variations of reference evapotranspiration (ET_o) and quantifying any trends offer valuable information for regional hydrology, agricultural water requirements and water resources management. This study aimed to examine the temporal trends in the Penman–Monteith ET_o in the west and southwest of Iran by using the Kendall and Spearman tests after eliminating the influence of significant lag-1 serial correlation from the ET_o time series. The magnitudes and starting years of significant ET_o trends were determined by the Mann-Kendall rank statistic and the Theil–Sen’s estimator, respectively. For the study period of 1966–2005, a significant positive lag-1 serial correlation coefficient was observed at almost all the stations. The existence of the positive serial correlation in the ET_o series increased the possibility of the Kendall and Spearman tests to reject the null hypothesis of no trend while it is true. It was found that the Kendall test was more sensitive than the Spearman test to the existence of the positive serial correlation in the ET_o series. After removing the serial correlation effect with pre-whitening method, only three significant increasing ET_o trends were obtained at Khorram-Abad, Shahrekord and Zanjan stations at the rates of 0.16, 0.06 and 0.06 mm/day per decade, respectively. The significant increasing ET_o trends of Khorram-Abad, Zanjan and Shahrekord stations started in 1997, 1994 and 1998, respectively. The stepwise regression method showed that wind speed was the most dominating variable affecting on the significant changes of ET_o.     

Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models

Water Resources Management - Water resources in arid and semi-arid regions are susceptible to alteration in hydro-climatic variables, especially under climate change which makes runoff simulations...     

Effects of nanoclay and coupling agent on the physico‐mechanical,morphological, and thermal properties of wood flour/polypropylene composites

This article presents the effects of coupling agent and nanoclay (NC) on some properties of wood flour/polypropylene composites. The composites with different NC and maleic anhydride grafted polypropylene (MAPP) contents were fabricated by melt compounding in a twin‐screw extruder and then by injection molding. The mass ratio of the wood flour to polymer was 40/60 (w/w). Results showed that applying MAPP on the surface of the wood flour can promote filler polymer interaction, which, in turn, would improve mechanical properties of the composite as well as its water uptake and thermal stability. Composite voids and the lumens of the fibers were filled with NC, which prevented the penetration of water by the capillary action into the deeper parts of composite. Therefore, the water absorption in composites fabricated using NC was significantly reduced. Scanning electron microscopy has shown that the treatment of composites with 5 wt% MAPP, promotes better fiber–matrix interaction, resulting in a few numbers of pull‐out traces. In all cases, the degradation temperatures shifted to higher values after using MAPP. The largest improvement on the thermal stability of composites was achieved when NC was added. POLYM. ENG. SCI., 2011. © 2010 Society of Plastics Engineers     

Application of Genetic Algorithms and Artificial Neural Networks in Conjunctive Use of Surface and Groundwater Resources

Abstract

In this paper, a methodology for conjunctive use of surface and groundwater resources is developed using the combination of the Genetic Algorithms (GAs) and the Artificial Neural Networks (ANN). Water supply to agricultural demands, reduction of pumping costs and control of groundwater table fluctuations are considered in the objective function of the model. In the proposed model, the results of MODFLOW groundwater simulation model are used to train an ANN. The ANN as groundwater response functions is then linked to the GA based optimization model to develop the monthly conjunctive use operating policies. The model is applied to the surface and groundwater allocation for irrigation purposes in the southern part of Tehran. A new ANN is also trained and checked for developing the real-time conjunctive use operating rules.

The results show the significance of an integrated approach to surface and groundwater allocation in the study area. A simulation of the optimal policies shows that the cumulative groundwater table variation can be reduced to less than 4 meters from the current devastating condition. The results also show that the proposed model can effectively reduce the run time of the conjunctive use models through the composition of a GA-based optimization and a ANN-based simulation model.     

Multi-Objective Optimal Model for Conjunctive Use Management Using SGAs and NSGA-II Models

The widespread investigations on water resources management has become an essential issue because due to lack of sufficient research and inattention to planning and management of conjunctive use of surface and groundwater. The conjunctive management is a suitable alternative for imbalanced water resources distribution and related constraints in using of surface water. In this paper, a multi-objective model is developed to maximize the minimum reliability of system as well as minimize the costs due to water supply, aquifer reclamation and violation of the reservoir capacity in operation and allocation priority. The non-dominated sorting genetic algorithm (NSGA-II) is used to present the optimal trade-off between the objectives. The sequential genetic algorithms is also applied (SGA) in order to be compared with the NSGA-II model. The results show that the NSGA-II model can considerably reduce the computation burden of the conjunctive use models in comparison with the SGA optimization model. The obtained trade-off curve shows that a little increase in reliability leads to much more system costs. The weighted single objective SGA model results verify optimal trade-off obtained from NSGA-II model and show the optimality of allocated discharges.     

Development a Novel Integrated Distributed Multi-objective Simulation-optimization Model for Coastal Aquifers Management Using NSGA-II and GMS Models

Water Resources Management - Groundwater is one of the most valuable water resources in the world in terms of quantity and quality. Therefore, their protection as an important issue should be...     

Flood flow forecasting using ANN,ANFIS and regression models

Flood prediction is an important for the design, planning and management of water resources systems. This study presents the use of artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), multiple linear regression (MLR) and multiple nonlinear regression (MNLR) for forecasting maximum daily flow at the outlet of the Khosrow Shirin watershed, located in the Fars Province of Iran. Precipitation data from four meteorological stations were used to develop a multilayer perceptron topology model. Input vectors for simulations included the original precipitation data, an area-weighted average precipitation and antecedent flows with one- and two-day time lags. Performances of the models were evaluated with the RMSE and the R ². The results showed that the area-weighted precipitation as an input to ANNs and MNLR and the spatially distributed precipitation input to ANFIS and MLR lead to more accurate predictions (e.g., in ANNs up to 2.0 m³ s^?1 reduction in RMSE). Overall, the MNLR was shown to be superior (R ² = 0.81 and RMSE = 0.145 m³ s^?1) to ANNs, ANFIS and MLR for prediction of maximum daily flow. Furthermore, models including antecedent flow with one- and two-day time lags significantly improve flow prediction. We conclude that nonlinear regression can be applied as a simple method for predicting the maximum daily flow.