This Research Topic is part of a series. To view the other sessions, please follow the links below:
Numerical models are common tools for water resources problems. They are used in theoretical research for gaining insights regarding the physics and fully understanding the complex processes controlling water resources problems. Numerical models are also used in practical applications at different scales for several purposes such as predictive studies, management, designing systems and making decisions. Field applications of numerical models require efficient simulators to deal with large time and space scales and a calibration procedure that aims at fitting simulations and field observations. Usually, these steps require a sensitivity analysis to identify the most significant parameters and uncertainty analysis to evaluate the effects of the imperfect knowledge of input parameters on model outputs. Despite the significant progress in model development and applications, several research questions are still open on the improvement of model applicability and reliability. These questions will last in order to cover new applications and to take advantage of the continued improvements in computing technology.
This Research Topic focuses on the latest advances, developments and applications of numerical models for water resources problems. This includes, but is not limited to: surface flow, surface-subsurface interaction, saturated/unsaturated flow, multiphase flow, multicomponent reactive transport, heat transfer and coupled hydraulic, thermal, mechanical, chemical and biological processes.
A non-exhaustive list of possible contributions includes:
• Mathematical development for improving models realism by considering/coupling new physical processes;
• Multi-physics modeling and simulation studies for new physical insights;
• Development of new numerical methods and techniques to improve models applicability and capacity;
• Numerical algorithms and computer implementation;
• Stochastic and probabilistic modeling;
• New analytical and semi-analytical solutions for benchmarking purposes;
• Uncertainty and sensitivity analysis;
• Lab experimental studies and confrontation with numerical simulations; and,
• Model applications in field studies.
This Research Topic is part of a series. To view the other sessions, please follow the links below:
Numerical models are common tools for water resources problems. They are used in theoretical research for gaining insights regarding the physics and fully understanding the complex processes controlling water resources problems. Numerical models are also used in practical applications at different scales for several purposes such as predictive studies, management, designing systems and making decisions. Field applications of numerical models require efficient simulators to deal with large time and space scales and a calibration procedure that aims at fitting simulations and field observations. Usually, these steps require a sensitivity analysis to identify the most significant parameters and uncertainty analysis to evaluate the effects of the imperfect knowledge of input parameters on model outputs. Despite the significant progress in model development and applications, several research questions are still open on the improvement of model applicability and reliability. These questions will last in order to cover new applications and to take advantage of the continued improvements in computing technology.
This Research Topic focuses on the latest advances, developments and applications of numerical models for water resources problems. This includes, but is not limited to: surface flow, surface-subsurface interaction, saturated/unsaturated flow, multiphase flow, multicomponent reactive transport, heat transfer and coupled hydraulic, thermal, mechanical, chemical and biological processes.
A non-exhaustive list of possible contributions includes:
• Mathematical development for improving models realism by considering/coupling new physical processes;
• Multi-physics modeling and simulation studies for new physical insights;
• Development of new numerical methods and techniques to improve models applicability and capacity;
• Numerical algorithms and computer implementation;
• Stochastic and probabilistic modeling;
• New analytical and semi-analytical solutions for benchmarking purposes;
• Uncertainty and sensitivity analysis;
• Lab experimental studies and confrontation with numerical simulations; and,
• Model applications in field studies.