Development of an efficient surrogate model based on aquifer dimensions to prevent seawater intrusion in anisotropic coastal aquifers,case study: the Qom aquifer in Iran

Coastal aquifers are usually exposed to saltwater intrusion. Therefore, groundwater extracted from these aquifers should be regulated considering their dimensions and effective parameters. In this paper, optimum discharge from a large number of exploitation wells is evaluated according to variations of width, length, and anisotropy coefficient in the Qom aquifer near the salt lake in central Iran. First, the wells are divided into clusters to decrease the number of decision variables. Then, the location and discharge from each cluster is obtained using SEAWAT and charged system search (CSS) simulation–optimization model with the assumption of three-dimensional variable density flow. The maximum discharge considering various anisotropy rates is computed based on different values of lengths and widths of the aquifer. Finally, an M5-tree model is trained using the obtained samples to derive a linear relationship between input and output data. Based on the results, for various ranges of width and length of an aquifer with impermeable boundaries, different linear equations for optimum discharge are obtained. Also, it was found that for an aquifer with a small width, the critical discharge is a function of the length while the effect of the boundaries is negligible. Sensitivity analysis of the anisotropy coefficient reveals that with increasing the anisotropy rate, thickness and slope of the transition zone decrease and as the maximum discharge increases consequently. However, the sensitivity of the discharge to anisotropy rate is not remarkable. A comparison between the results of this study with those of the analytical method based on sharp interface assumption is carried out. For the critical condition, the best agreement between analytical equation (\(\overline {L} =0.87\overline {W} +0.62\)) and proposed method (\(\overline {L} =0.83\overline {W} - 1.41\)) is achieved for the anisotropic aquifer when the 50% isochlor is assumed as the measure of salt water intrusion.     

Multi-objective Freshwater Management in Coastal Aquifers Under Uncertainty in Hydraulic Parameters

Natural Resources Research - This paper proposes a novel stochastic framework for groundwater quantity and quality management in aquifers threatened by saltwater intrusion. In this methodology, a...     

Active Control of A Piston-Type Absorbing Wavemaker with Fully Reflective Structure

China Ocean Engineering - Multiple reflections of the waves between structure and wavemaker in hydraulic flumes could change the frequency content of the desired incident wave or result in...     

Monthly karstic spring flow forecasting using a sequential gaussian simulation technique

Analyzing groundwater hydrologic equations related to karstic aquifers and spring hydrograph simulation have become the focus of many researches. Having double or triple porosity structure, mixed flow nature, and varying conduit permeability have made these formations become complex heterogenic systems with great temporal and spatial hydrodynamic variability. In this paper, a conditional sequential gaussian simulation (SGS) is used to simulate monthly flow data of five karstic springs with different hydrogeological properties, located in Zagros Mountain Chain, in western Iran. To evaluate the performance of the SGS algorithm, the results are compared with those of an autoregressive integrated moving average (ARIMA) model. The results demonstrate the efficiency of the SGS model in simulation of monthly flows compared to the ARIMA model. They also show the suitability of this model for handling uncertainty associated with karstic spring flows through generation of several equally probable stochastic realizations.     

A spatiotemporal Bayesian maximum entropy-based methodology for dealing with sparse data in revising groundwater quality monitoring networks: the Tehran region experience

Data inadequacy is a common problem in designing or updating groundwater monitoring systems. The developed methodologies for the optimal design of groundwater monitoring systems usually assume that there is a complete set of data obtained from existing monitoring wells and provide a revised configuration for the system by analyzing the current data. These methodologies are not usually applicable when the current groundwater quantity and quality data are highly sparse. In this paper, a new simulation–optimization approach based on Bayesian maximum entropy theory (BME) is proposed for revising spatial and temporal monitoring frequencies in a sparsely monitored aquifer. The BME is used to simulate the spatial and spatiotemporal variations of groundwater indicators, incorporating the space/time uncertainties due to insufficient data. Comparing the obtained estimations with observations, the best BME model was selected to be linked with an optimization model. The main goal of optimization was to find out the spatial and temporal sampling characteristics of the monitoring stations using the concepts of Entropy theory and a groundwater vulnerability index. The results show the BME estimations are less biased and more accurate than Ordinary Kriging in both spatial and spatiotemporal analysis. The improvements in the BME estimates are mostly related to incorporating hard (accurate) and soft (uncertain) data in the estimation process. The applicability and efficiency of the proposed methodology have been evaluated by applying it to the Tehran aquifer in Iran which is suffering from high groundwater table fluctuations and nitrate pollution. Based on the results, in addition to the existing monitoring wells, seven new monitoring stations have been proposed. Few stations which potentially can be removed or combined with other stations have been identified and a monthly sampling frequency has been suggested.     

Sensitivity and fuzzy uncertainty analyses in the determination of SCS-CN parameters from rainfall–runoff data

Spatial and seasonal variations of curve number (CN) and initial abstraction ratio (λ) in a watershed can result in inaccurate runoff volume estimations when using the US Natural Resources Conservation Service (SCS-CN) method with constant values for these parameters. In this paper, parameters of CN and λ are considered as calibration parameters and the sensitivity of estimated runoff to these parameters using the SCS-CN method is scrutinized. To incorporate the uncertainty associated with CN and λ, fuzzy linear regression (FLR) is applied to derive the relationships of CN and λ with rainfall depth (P) by employing a large dataset of storm events from four watersheds in Iran. Results indicate that the proposed approach provides more accuracy in estimation of runoff volume compared to the SCS method with constant values of CN and λ, and gives a straightforward technique for evaluating the hydrological effects of CN, λ, and P on runoff volume.     

Evaluating sampling locations in river water quality monitoring networks: application of dynamic factor analysis and discrete entropy theory

In this paper, a methodology is proposed for evaluating sampling locations in an existing river water quality monitoring network. The dynamic factor analysis is utilized to extract the independent dynamic factors from time series of water quality variables. Then, the entropy theory is applied to the independent dynamic factors to construct transinformation–distance (T–D) curves. The computation time in the case of using dynamic factors is significantly less than when the raw data is used because the number of independent dynamic factors is usually much less than the number of monitored water quality variables. In this paper, it is also shown that by clustering the study area to some homogenous zones and developing T–D curves for each zone, the accuracy of the results is significantly increased. To evaluate the applicability and efficiency of the proposed methodology, it is applied to the Karoon River which is the most important river system in Iran.     

Development of a master plan for industrial solid waste management

Rapid industrial growth in the province of Khuzestan in the south west of Iran has resulted in disposal of about 1750 tons of solid waste per day. Most of these industrial solid wastes including hazardous wastes are disposed without considering environmental issues. This has contributed considerably to the pollution of the environment. This paper introduces a framework in which to develop a master plan for industrial solid waste management. There are usually different criteria for evaluating the existing solid waste pollution loads and how effective the management schemes are. A multiple criteria decision making technique, namely Analytical Hierarchy Process (AHP), is used for ranking the industrial units based on their share in solid waste related environmental pollution and determining the share of each unit in total solid waste pollution load. In this framework, a comprehensive set of direct, indirect, and supporting projects are proposed for solid waste pollution control. The proposed framework is applied for industrial solid waste management in the province of Khuzestan in Iran and a databank including GIS based maps of the study area is also developed. The results have shown that the industries located near the capital city of the province, Ahwaz, produce more than 32 percent of the total solid waste pollution load of the province. Application of the methodology also has shown that it can be effectively used for development of the master plan and management of industrial solid wastes.