Numerical modeling of groundwater flow and nitrate transport using MODFLOW and MT3DMS in the Karaj alluvial aquifer, Iran

Shakeri, Reza; Nassery, Hamid Reza; Ebadi, Taghi

doi:10.1007/s10661-022-10881-4

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…However, for transient-state calibration, the model was calibrated at a monthly time step for 2020 for 13 piezometers. The hydraulic parameters, including saturated hydraulic conductivity, recharge rate, stream bed conductivity, specific yield, and specific storage, were manually adjusted using a trial-and-error process during calibration [47,48]. The goodness of fit of the observed and simulated SGW head was assessed by the coefficient of determination (R 2 ), root mean square error (RMSE), and mean error (ME).…”

Section: Sgw Flow Model Calibration and Validation Proceduresmentioning

confidence: 99%

“…The goodness of fit of the observed and simulated SGW head was assessed by the coefficient of determination (R 2 ), root mean square error (RMSE), and mean error (ME). RMSE and ME values near zero indicate better model performance [23,41,48,49]. R 2 values range from 0 to 1, where close to 1 represents a good fit between observed data and model-simulated output [41,48,49].…”

Section: Sgw Flow Model Calibration and Validation Proceduresmentioning

confidence: 99%

“…RMSE and ME values near zero indicate better model performance [23,41,48,49]. R 2 values range from 0 to 1, where close to 1 represents a good fit between observed data and model-simulated output [41,48,49]. After satisfactory goodness of fit was achieved for calibration, the simulated SGW head was validated monthly for 2021.…”

Section: Sgw Flow Model Calibration and Validation Proceduresmentioning

confidence: 99%

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Using MODFLOW to Model Riparian Wetland Shallow Groundwater and Nutrient Dynamics in an Appalachian Watershed

Abesh,

Anderson,

Hubbart

2024

Water

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Simulating shallow groundwater (SGW) flow dynamics and stream–SGW interactions using numerical modeling tools is necessary to develop a mechanistic understanding of water flow systems and improve confidence in water resource management practices. A three-dimensional (3D) SGW flow model was developed for a riparian wetland in a mixed forest and agricultural catchment in West Virginia (WV), Appalachia, USA, using a Modular 3D Groundwater Model (MODFLOW). The MODFLOW simulation was calibrated in steady (R2 = 0.98, ME = −0.21, and RMSE = 0.77), transient state (R2 = 0.97, ME = −0.41, and RMSE = 1.28) and validated (R2 = 0.97, ME = −0.28, and RMSE = 1.05) using observed SGW levels from thirteen nested piezometers under steady and transient states. An experimental MT3D transport scenario was developed to show the lateral transport of NO₃-N from the aquifer to stream cells. Relatively stable SGW head distribution was observed. In the downstream reach, SGW discharge varied from 948 m3/day to 907 m3/day in 2020, with creek seepage ranging from 802 m3/day to 790 m3/day. Similarly, SGW input to the stream ranged from 891 m3/day to 978 m3/day, while creek seepage ranged from 796 m3/day to 800 m3/day in 2021. In upstream reaches, losing stream conditions were observed in January, June, and September 2020 and January to April 2021, while gaining stream conditions prevailed during other months. Thus, an approximately monthly alternating gaining–losing stream condition was observed in the upstream area. An experimental MT3D transport scenario resulted in an advection–dispersion scenario, showing a cumulative loss of 947 g of NO3-N from SGW to the stream. Denitrification accounted for the cumulative loss of 1406 g of NO3-N from SGW, surpassing 639 g of nitrate from the SGW to the stream during the study period. Additionally, particle tracking using MODPATH indicated a long residence time for SGW nutrients, affirming the efficiency of nitrogen transformation through denitrification. This study is among the first to simulate hydrologic and nutrient interactions in riparian wetlands of a mixed land use catchment in the Appalachian region of the northeastern United States. The results better inform water resource management decisions and modeling efforts in the Appalachian region and similar physiographic regions globally.

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Section: Sgw Flow Model Calibration and Validation Proceduresmentioning

confidence: 99%

Section: Sgw Flow Model Calibration and Validation Proceduresmentioning

confidence: 99%

Section: Sgw Flow Model Calibration and Validation Proceduresmentioning

confidence: 99%

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Using MODFLOW to Model Riparian Wetland Shallow Groundwater and Nutrient Dynamics in an Appalachian Watershed

Abesh,

Anderson,

Hubbart

2024

Water

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“…This discretization is often achieved through finite difference or finite element methods, and the process is iteratively repeated at each time step. These numerical models are extensively employed in aquifer resource management (Omar et al 2020;Miro et al 2021;Ostad-Ali-Askari & Shayannejad 2021), design of aquifer storage and recovery schemes (LaHaye et al 2021;Tiwari et al 2022Tiwari et al , 2023, drought risk management (Shivakoti et al 2019;Wossenyeleh et al 2021), and aquifer pollution control (Robinson et al 2009;Guo et al 2021;Panjehfouladgaran & Rajabi 2022;Shakeri et al 2023). However, the computational expense of numerical methods used in regional groundwater simulations can pose a significant obstacle for tasks that involve repeated simulations, such as uncertainty analysis and simulation-based optimization.…”

Section: Introductionmentioning

confidence: 99%

Accelerating regional-scale groundwater flow simulations with a hybrid deep neural network model incorporating mixed input types: A case study of the northeast Qatar aquifer

Al-Maktoumi,

Rajabi,

Zekri

et al. 2024

Journal of Hydroinformatics

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This study presents the ‘Dual Path CNN-MLP’, a novel hybrid deep neural network (DNN) architecture that merges the strengths of convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) for regional groundwater flow simulations. This model stands out from previous DNN approaches by managing mixed input types, including both imagery and numerical vectors. Such flexibility allows the diverse nature of groundwater data to be efficiently utilized without the need to convert it into a uniform format, which often leads to oversimplification or unnecessary expansion of the dataset. When applied to the northeast Qatar aquifer, the model demonstrates high accuracy in simulating transient groundwater flow fields, benchmarked against the well-established MODFLOW model. The model's efficacy is confirmed through k-fold cross-validation, showing an error margin of less than 12% across all examined locations. The study also examines the model's ability to perform uncertainty analysis using Monte Carlo simulations, finding that it achieves around 1% average absolute percentage error in estimating the mean hydraulic head. Errors are mostly found in areas with significant variations in the hydraulic head. Switching to this ML model from the conventional MODFLOW simulator boosts computational efficiency by about 99%, showcasing its advantage for tasks like uncertainty analysis in repetitive groundwater simulations.

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Assessment of future groundwater levels using Visual MODFLOW in the Gomti River basin in India

Das,

Singh,

Thakur

et al. 2023

Theor Appl Climatol

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Numerical modeling of groundwater flow and nitrate transport using MODFLOW and MT3DMS in the Karaj alluvial aquifer, Iran

Cited by 7 publications

References 33 publications

Using MODFLOW to Model Riparian Wetland Shallow Groundwater and Nutrient Dynamics in an Appalachian Watershed

Using MODFLOW to Model Riparian Wetland Shallow Groundwater and Nutrient Dynamics in an Appalachian Watershed

Accelerating regional-scale groundwater flow simulations with a hybrid deep neural network model incorporating mixed input types: A case study of the northeast Qatar aquifer

Assessment of future groundwater levels using Visual MODFLOW in the Gomti River basin in India

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