Correction of digital elevation models (DEMs) for flat areas is a critical process for hydrological analyses and modeling, such as the determination of flow directions and accumulations, and the delineation of drainage networks and sub-basins. In this study, a new algorithm is proposed for flat correction/removal. It uses the puddle delineation (PD) program to identify depressions (including their centers and overflow/spilling thresholds), compute topographic characteristics, and further fill the depressions. Three different levels of elevation increments are used for flat correction. The first and second level of increments create flows toward the thresholds and centers of the filled depressions or flats, while the third level of small random increments is introduced to cope with multiple threshold conditions. A set of artificial surfaces and two real-world landscapes were selected to test the new algorithm. The results showed that the proposed method was not limited by the shapes, the number of thresholds, and the surrounding topographic conditions of flat areas. Compared with the traditional methods, the new algorithm simplified the flat correction procedure and reduced the final elevation increments by 5.71–33.33%. This can be used to effectively remove/correct topographic flats and create flat-free DEMs.

Solute transport modeling resolves advection, dispersion, and chemical reactions in groundwater systems with its accuracy depending on the resolution of domain at all scales, thus the computational efficiency of a simulator becomes a bottleneck for the wide application of numerical simulations. However, the traditional serial numerical simulators have reached their limits for the prohibitive computational time and memory requirement in solving large-scale problems. These limitations have greatly hindered the wide application of groundwater solute transport modeling. Thus, the development of an efficient method for handling large-scale groundwater solute transport simulation is urgently required. In this study, we developed and assessed a parallelized MT3DMS (Modular Three-Dimensional Multispecies Transport Model) by using OpenMP (Open specifications for Multi-Processing) to accelerate the solute transport simulation process. The parallelization was achieved by adding OpenMP compile directives (i.e., defining various types of parallel regions) into the most time-consuming packages, including the Advection package (ADV), Dispersion package (DSP), and Generalized Conjugate Gradient Solver package (GCG). This allows parallel processing on shared-memory multiprocessors, i.e., both the memory requirement and computing efforts are automatically distributed among all processors. Moreover, we discussed two different parallelization strategies for handling numerical models with either many layers or few layers. The performance of parallelized MT3DMS was assessed by two benchmark numerical models with different model domain sizes via a workstation with two quad-core processors. Results showed that the running time of parallelized MT3DMS can be 4.15 times faster than that using sequential MT3DMS. The effects of using different preconditioners (procedures that transform a given problem into a form that is more suitable for numerical solving methods) in the GCG package were additionally evaluated. The modified strategy for handling numerical models with few layers also achieved satisfactory results with running time two times faster than that via sequential simulation. Thus, the proposed parallelization allows high-resolution groundwater transport simulation with higher efficiency for large-scale or multimillion-cell simulation problems.

To overcome the large time and memory consumption problems in large-scale high-resolution contaminant transport simulations, an efficient approach was presented to parallelize the modular three-dimensional transport model for multi-species (MT3DMS) (University of Alabama, Tuscaloosa, AL, USA) program on J adaptive structured meshes applications infrastructures (JASMIN). In this approach, a domain decomposition method and a stencil-based method were used to accomplish parallel implementation, while a ghost cell strategy was used for communication. The MODFLOW-MT3DMS coupling mode was optimized to achieve the parallel coupling of flow and contaminant transport. Five types of models were used to verify the correctness and test the parallel performance of the method. The developed parallel program JMT3D (China University of Geosciences (Beijing), Beijing, China) can increase the speed by up to 31.7 times, save memory consumption by 96% with 46 processors, and ensure that the solution accuracy and convergence do not decrease as the number of domains increases. The BiCGSTAB (Bi-conjugate gradient variant algorithm) method required the least amount of time and achieved high speedup in most cases. Coupling the flow and contaminant transport further improved the efficiency of the simulations, with a 33.45 times higher speedup achieved on 46 processors. The AMG (algebraic multigrid) method achieved a good scalability, with an efficiency above 100% on hundreds of processors for the simulation of tens of millions of cells.