MODFLOW 6 is the latest in a line of six “core” versions of MODFLOW released by the U.S. Geological Survey. The MODFLOW 6 architecture supports incorporation of additional hydrologic processes, in addition to groundwater flow, and allows interaction between processes. The architecture supports multiple model instances and multiple types of models within a single simulation, a flexible approach to formulating and solving the equations that represent hydrologic processes, and recent advances in interoperability, which allow MODFLOW to be accessed and controlled by external programs. The present version of MODFLOW 6 consolidates popular capabilities available in MODFLOW variants, such as the unstructured grid support in MODFLOW‐USG, the Newton‐Raphson formulation in MODFLOW‐NWT, and the support for partitioned stress boundaries in MODFLOW‐CDSS. The flexible multi‐model capability allows users to configure MODFLOW 6 simulations to represent the local‐grid refinement (LGR) capabilities available in MODFLOW‐LGR, the multi‐species transport capabilities in MT3DMS, and the coupled variable‐density capabilities available in SEAWAT. This paper provides a new, holistic and integrated overview of simulation capabilities made possible by the MODFLOW 6 architecture, and describes how ongoing and future development can take advantage of the program architecture to integrate new capabilities in a way that is minimally invasive and automatically compatible with the existing MODFLOW 6 code.

MODFLOW as a Configurable Multi-Model Hydrologic Simulator.

ABSTRACT Accurate, stable, and long-term water quality predictions are essential for water pollution warning and efficient water environment management. In this study, a hierarchical water quality prediction (HWQP) model was developed based on ‘data decomposition–predictor screening–efficient prediction’ via wavelet decomposition, Spearman correlation analysis, and long short-term memory network, respectively. The observed data from 14 stations in the Huaihe River–Hongze Lake system, including ammonia nitrogen (AN) and chemical oxygen demand (COD), were used to make long-term water quality predictions. The results suggested that, compared to existing water quality prediction models, the HWQP model has higher accuracy, with the root mean square errors of 6 and 17% for simulating AN and COD, respectively. The AN and COD concentrations will range from 0 to 1 mg/l and from 3 to 5 mg/l at 12 stations, respectively, and the COD concentrations will exceed the water quality target at Stations 4 and 5. The established model has great potential to address the challenges associated with the water environment. 

A novel hybrid model for long-term water quality prediction with the ‘decomposition–inputs–prediction’ hierarchical optimization framework

The simulation-optimization method, integrating the numerical model and the evolutionary algorithm, is increasingly popular for identifying the release history of groundwater contaminant sources. However, due to the usage of computationally intensive evolutionary algorithms, traditional simulation-optimization methods always require thousands of simulations to find appropriate solutions. Such methods yield a prohibitive computational burden if the simulation involved is time-consuming. To reduce general computation, this study proposes a novel simulation-optimization method for solving the inverse contaminant source identification problems, which uses surrogate models to approximate the numerical model. Unlike many existing surrogate-assisted methods using the pre-determined surrogate model, this paper presents an adaptive surrogate technique to construct the most appropriate surrogate model for the current numerical model. Two representative cases about identifying the release history of contaminant sources are used to investigate the accuracy and robustness of the proposed method. The results indicate that the proposed adaptive surrogate-assisted method effectively identifies the release history of groundwater contaminant sources with a higher degree of accuracy and shorter computation time than traditional methods.

/pdf/an-adaptive-surrogate-assisted-simulation-optimization-1k26ta5z.pdf

An Adaptive Surrogate-Assisted Simulation-Optimization Method for Identifying Release History of Groundwater Contaminant Sources

Understanding variations in contaminant concentrations and exploring their driving factors are essential for pollution control and water environment improvement. The Huaihe River Basin, as an important region in the eastern region of China, has attracted much attention to its water environment issues in recent years. Therefore, an in-depth analysis of spatiotemporal patterns of water quality parameters was carried out on the Huaihe River–Hongze Lake system, for the period 1998–2018, using the Mann–Kendall test (MKT) and wavelet transforms (WTs). Significant decrease trends of ammonia nitrogen (AN) and chemical oxygen demand (COD) concentrations were detected in the Huaihe River (HR) before 2008 using the MKT. High concentration in the contaminant load was a result of the effect of increased construction and decreased forest on increasing input of pollutants during this period. The results of the WT showed how factors (e.g., streamflow and water temperature), except land use, affect the variations in AN and COD concentrations. The comparison of spatiotemporal patterns of AN and COD between the HR and Hongze Lake (HL) showed their differences in contaminant transport regimes. The contaminants were rapidly transported downstream along the HR with high streamflow during the wet season, while these in the HL were less responsive due to the long residence time of the water body. In addition, rebounds of contaminant concentrations occurred many times at the confluence between the HR and the HL due to strong river–lake interactions, especially in the flood season. These results have implications for future water environment management in the Huaihe River Basin and in similar settings worldwide.

/pdf/spatiotemporal-patterns-of-ammonia-nitrogen-and-chemical-2himy2ar.pdf

Spatiotemporal Patterns of Ammonia Nitrogen and Chemical Oxygen Demand in the Huaihe River–Hongze Lake System (Eastern China)

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An Adaptive Surrogate-Assisted Simulation-Optimization Method for Identifying Release History of Groundwater Contaminant Sources

Spatiotemporal Patterns of Ammonia Nitrogen and Chemical Oxygen Demand in the Huaihe River–Hongze Lake System (Eastern China)