The combined use of MODFLOW and precipitation-runoff modeling to simulate groundwater flow in a diffuse-pollution prone watershed

Abstract

A numerical modeling case study of groundwater flow in a diffuse pollution prone area is presented. The study area is located within the metropolitan borders of the city of Izmir, Turkey. This groundwater flow model was unconventional in the application since the groundwater recharge parameter in the model was estimated using a lumped, transient water-budget based precipitation-runoff model that was executed independent of the groundwater flow model. The recharge rate obtained from the calibrated precipitation-runoff model was used as input to the groundwater flow model, which was eventually calibrated to measured water table elevations. Overall, the flow model results were consistent with field observations and model statistics were satisfactory. Water budget results of the model revealed that groundwater recharge comprised about 20% of the total water input for the entire study area. Recharge was the second largest component in the budget after leakage from streams into the subsurface. It was concluded that the modeling results can be further used as input for contaminant transport modeling studies in order to evaluate the vulnerability of water resources of the study area to diffuse pollution.

Application of the Hybrid HS–Solver Algorithm to the Solution of Groundwater Management Problems

“…An areal, steady-state groundwater flow model was previously developed by Elçi et al (2010) to calculate groundwater fluxes and the water budget for the surficial aquifer of the Tahtalı watershed. This one-layered model is set up for steady-state groundwater flow conditions and is based on MODFLOW.…”

Groundwater Recharge Rate and Zone Structure Estimation Using PSOLVER Algorithm

“…The main criterion for obtaining the best recharge zone structure is to minimize the root mean squared error () between the simulated and observed hydraulic head values at the monitoring wells shown in Figure . At the same time, as an additional means to constrain the optimization, the sum of recharge rates for the nodes that coincide with the Tahtalı Watershed area (light gray‐shaded area in Figure or zone 4 in Figure 5 h) is forced to be equal to the areal recharge rate that was determined in the previous study by Elçi et al (). The hydraulic conductivity zones and values were exactly the same as in the previous study, and no optimization is performed on these parameters to be able to compare the identification results.…”

“…The second penalty function (Equation 6) is introduced to satisfy the constraint of areal groundwater recharge rate of the Tahtalı Watershed (light gray‐shaded region in Figure ), which was calculated in the previous modeling study by Elçi et al () using a water budget based, transient precipitation‐runoff model. In this study, represents the recharge rate for the area representing the Tahtalı Watershed and is conceived as an information that is already known.…”

“…Then, the spreadsheet Solver takes over and solves the problem again to improve the current solution. For all the solutions, bounds of the recharge rates are defined as ( k = 1, 2, 3, …, n z ), as it was previously used by Elçi et al () in their automated model calibration. Note that the row and column numbers of the finite difference grid are used to define the locations of basis points.…”

Advances in GIS-Based Approaches to Groundwater Vulnerability Assessment: Overview and Applications

“…aquifer and soil type) based on a scale from 1 to 10, depending on the degree of groundwater vulnerability. Groundwater depth is calculated using cokriging interpolation on seasonal groundwater levels at 31 monitoring points [ 8 ] . The collocated auxiliary data used in the cokriging process is selected as the ground surface elevation data from the DEM of the study area.…”