Development of a Parallel Computational Framework to Solve Flow and Transport in Integrated Surface-Subsurface Hydrologic Systems

HydroGeoSphere (HGS) is a 3D control-volume finite element hydrologic model describing fully-integrated surface-subsurface water flow and solute and thermal energy transport. Because the model solves tightly-coupled highly-nonlinear partial differential equations, often applied at regional and continental scales (for example, to analyze the impact of climate change on water resources), high performance computing (HPC) is essential. The target parallelization includes the composition of the Jacobian matrix for the iterative linearization method and the sparse-matrix solver, preconditioned BiCGSTAB. The Jacobian matrix assembly is parallelized by using a static scheduling scheme with taking account into data racing conditions, which may occur during the matrix construction. The parallelization of the solver is achieved by partitioning the domain into equal-size sub-domains, with an efficient reordering scheme. The computational flow of the BiCGSTAB solver is also modified to reduce the parallelization overhead and to be suitable for parallel architectures. The parallelized model is tested on several benchmark cases that include linear and nonlinear problems involving various domain sizes and degrees of hydrologic complexity. The performance is evaluated in terms of computational robustness and efficiency, using standard scaling performance measures. Simulation profiling results indicate that the efficiency becomes higher for three situations: 1) with an increasing number of nodes/elements in the mesh because the work load per CPU decreases with increasing the number of nodes, which reduces the relative portion of parallel overhead in total computing time., 2) for increasingly nonlinear transient simulations because this makes the coefficient matrix diagonal dominance, and 3) with domains of irregular geometry that increases condition number. These characteristics are promising for the large-scale analysis of water resource problems that involve integrated surface-subsurface flow regimes. Large-scale real-world simulations illustrate the importance of node reordering, which is associated with the process of the domain partitioning. With node reordering, super-scalarable parallel speedup was obtained when compared to a serial simulation performed with natural node ordering. The results indicate that the number of iterations increases as the number of threads increases due to the increased number of elements in the off-diagonal blocks in the coefficient matrix. In terms of the privatization scheme, the parallel efficiency with privatization was higher than that with the shared scheme for most of simulations performed

Earth Systems Modeling in the Brazos River Alluvium Aquifer: Improvement of Computational Methods and Development of Conceptual Model

Traditional hydrologic modeling has compartmentalized the water cycle into distinct components (e.g. Traditional hydrologic modeling has compartmentalized the water cycle into distinct components (e.g. rainfall-runoff, river routing, or groundwater flow models). In river valley alluvium aquifers, these processes are too interconnected to be represented accurately by separate models. An integrated modeling framework assesses two or more of these components simultaneously, reducing the error associated with approximated boundary conditions. One integrated model, ParFlow.CLM, offers the advantage of parallel computing, but it lacks any mechanism for incorporating time-varying streamflow as an upstream boundary condition. Previous studies have been limited to headwater catchments. Here, a generalized method is developed for applying transient streamflow at an upstream boundary in ParFlow.CLM. The upstream inflow method was successfully tested on two domains – one idealized domain with a straight channel, and one small stream catchment in the Brazos River Basin. The stream in the second domain is gaged at the upstream and downstream boundaries. Both tests assumed a homogeneous subsurface, so that the efficacy of the transient streamflow method could be evaluated with minimal complications by groundwater interactions. Additionally, an integrated conceptual model is presented for the Brazos River Alluvium Aquifer (BRAA), the Brazos River, and the overlying terrain. The BRAA is a floodplain aquifer in central to southeast Texas. This aquifer is highly connected to the Brazos River and experiences localized semi-confined conditions beneath thick surface clay layers. The conceptual model is designed to be implemented in an Earth system modeling framework and is limited to the central portion of the aquifer in Brazos and Burleson Counties, Texas. Unlike previous models in ParFlow.CLM, this is a high-order subbasin with large inflows from upstream. Additionally, the model incorporates no-flow, transient head, and free drainage boundaries. Preliminary tests suggest the need for a long spin-up period. Long-term simulations will require calibration of surface and subsurface parameters before using the model to assess system behavior

Enhancing speed and scalability of the ParFlow simulation code

Regional hydrology studies are often supported by high resolution simulations of subsurface flow that require expensive and extensive computations. Efficient usage of the latest high performance parallel computing systems becomes a necessity. The simulation software ParFlow has been demonstrated to meet this requirement and shown to have excellent solver scalability for up to 16,384 processes. In the present work we show that the code requires further enhancements in order to fully take advantage of current petascale machines. We identify ParFlow's way of parallelization of the computational mesh as a central bottleneck. We propose to reorganize this subsystem using fast mesh partition algorithms provided by the parallel adaptive mesh refinement library p4est. We realize this in a minimally invasive manner by modifying selected parts of the code to reinterpret the existing mesh data structures. We evaluate the scaling performance of the modified version of ParFlow, demonstrating good weak and strong scaling up to 458k cores of the Juqueen supercomputer, and test an example application at large scale.Comment: The final publication is available at link.springer.co

Integrated Environmental Modelling Framework for Cumulative Effects Assessment

Global warming and population growth have resulted in an increase in the intensity of natural and anthropogenic stressors. Investigating the complex nature of environmental problems requires the integration of different environmental processes across major components of the environment, including water, climate, ecology, air, and land. Cumulative effects assessment (CEA) not only includes analyzing and modeling environmental changes, but also supports planning alternatives that promote environmental monitoring and management. Disjointed and narrowly focused environmental management approaches have proved dissatisfactory. The adoption of integrated modelling approaches has sparked interests in the development of frameworks which may be used to investigate the processes of individual environmental component and the ways they interact with each other. Integrated modelling systems and frameworks are often the only way to take into account the important environmental processes and interactions, relevant spatial and temporal scales, and feedback mechanisms of complex systems for CEA. This book examines the ways in which interactions and relationships between environmental components are understood, paying special attention to climate, land, water quantity and quality, and both anthropogenic and natural stressors. It reviews modelling approaches for each component and reviews existing integrated modelling systems for CEA. Finally, it proposes an integrated modelling framework and provides perspectives on future research avenues for cumulative effects assessment

A synthetic analysis of integrated data fusion: Combining hydrologic and geophysical data collected during a tracer test to estimate aquifer flow and transport parameters

Integrated data fusion (IDF), also known as coupled inversion, is becoming a more widely used method for estimating hydrologic parameters from geophysical data. IDF is being used in this research as an approach to inversion that couples mathematical models of groundwater flow, solute transport, and electrical resistivity for the direct estimation of hydraulic conductivity, porosity, and dispersivity from transient resistivity data collected during a tracer test. In this work, synthetic field resistivity data are generated using only a single current electrode pair and many potential electrodes. This data is then used within the IDF framework to a) estimate hydraulic conductivity with a gradient-based optimization algorithm, b) analyze trends in hydraulic conductivity estimates related to changes in environmental and survey conditions, c) analyze model sensitivity to changes in hydraulic conductivity, porosity, and dispersivity, and d) determine if the limited resistivity data utilized are enough to infer that the initial conceptual model was incorrect. The results of the simulations indicate that hydraulic conductivity and porosity can be constrained quite well if Archie\u27s Law is known, but dispersivity may remain non-unique due to trade-offs with velocity and the spatial distribution of the plume. In addition, there may not be enough information contained within current/potential pair data to definitively rule out the possibility that the system is homogeneous; therefore the addition of more current pairs may be necessary