Assessing the impact of artificial recharge on groundwater in an overexploited aquifer: A case study in the Cheria Basin, North-East of Algeria

The Cheria region in Northeastern Algeria has been facing aquifer overexploitation by the agricultural sector and prolonged droughts,resulting in a considerable decline in groundwater levels.This study investigates the feasibility of implementing artificial recharge techniques to replenish the Eocene aquifer which serves as the primary water source in the Cheria region.A 3D transient numerical model,based on the finite difference method,was used to simulate groundwater flow from 2021 to 2031 using Visual MODFLOW Flex.During the modelling process,three scenarios were considered:(1)including pumping without a recharge,(2)recharge of the entire area through efficient infiltration without pumping,and(3)artificial recharge using river water infiltration basins at two sites,Draa Douamis sinkholes and Eocene limestone outcrops.The simulation results showed that aquifer exploitation without recharge caused significant drawdowns,which were 3 m to 7 m in the north-eastern part and 8 m to 12 m in the central and southern parts.In contrast,the second scenario,involving recharge without pumping,showed a rise in groundwater levels of 2 m to 2.7 m in the north-eastern part and 3 m to 3.62 m in the central and southern parts.The third scenario,employing artificial recharge,indicated a positive response to artificial recharge,with increased piezometric levels at the proposed sites,signifying a beneficial impact on the aquifer.These findings underline the potential of artificial recharge as a promising approach to address the groundwater depletion and environmental issues in the Cheria Basin.

Seepage safety monitoring model for an earth rock dam under influence of high-impact typhoons based on particle swarm optimization algorithm

Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam, a novel seepage safety monitoring model was constructed in this study. The nonlinear influence processes of the antecedent reservoir water level and rainfall were assumed to follow normal distributions. The particle swarm optimization （PSO） algorithm was used to optimize the model parameters so as to raise the fitting accuracy. In addition, a mutation factor was introduced to simulate the sudden increase in the piezometric level induced by short-duration heavy rainfall and the possible historical extreme reservoir water level during a typhoon. In order to verify the efficacy of this model, the earth rock dam of the Siminghu Reservoir was used as an example. The piezometric level at the SW1-2 measuring point during Typhoon Fitow in 2013 was fitted with the present model, and a corresponding theoretical expression was established. Comparison of fitting results of the piezometric level obtained from the present statistical model and traditional statistical model with monitored values during the typhoon shows that the present model has a higher fitting accuracy and can simulate the uprush feature of the seepage pressure during the typhoon perfectly.

Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect

Affected by external environmental factors and evolution of dam performance, dam seepage behavior shows nonlinear time-varying characteristics. In this study, to predict and evaluate the long-term development trend and short-term fluctuation of the dam seepage behavior, two monitoring models were developed, one for the base flow effect and one for daily variation of dam seepage elements. In the first model, to avoid the influence of the time lag effect on the evaluation of seepage variation with the time effect component of seepage elements, the base values of the seepage element and the reservoir water level were extracted using the wavelet multi-resolution analysis method, and the time effect component was separated by the established base flow effect monitoring model. For the development of the daily variation monitoring model for dam seepage elements, all the previous factors, of which the measured time series prior to the dam seepage element monitoring time may have certain influence on the monitored results, were considered. Those factors that were positively correlated with the analyzed seepage element were initially considered to be the support vector machine(SVM) model input factors, and then the SVM kernel function-based sensitivity analysis was performed to optimize the input factor set and establish the optimized daily variation SVM model. The efficiency and rationality of the two models were verified by case studies of the water level of two piezometric tubes buried under the slope of a concrete gravity dam.Sensitivity analysis of the optimized SVM model shows that the influences of the daily variation of the upstream reservoir water level and rainfall on the daily variation of piezometric tube water level are processes subject to normal distribution.