Filters: Tags: Parameter estimation (X)68 results (27ms)
Hydraulic gradient comparison method to estimate aquifer hydraulic parameters under steady-state conditions
Parameter and observation importance in modelling virus transport in saturated porous media—investigations in a homogenous system
This paper evaluates the importance of seven types of parameters to virus transport: hydraulic conductivity, porosity, dispersivity, sorption rate and distribution coefficient (representing physical–chemical filtration), and in-solution and adsorbed inactivation (representing virus inactivation). The first three parameters relate to subsurface transport in general while the last four, the sorption rate, distribution coefficient, and in-solution and adsorbed inactivation rates, represent the interaction of viruses with the porous medium and their ability to persist. The importance of four types of observations to estimate the virus-transport parameters are evaluated: hydraulic heads, flow, temporal moments of conservative-transport...
A modular approach to addressing model design, scale, and parameter estimation issues in distributed hydrological modelling
A modular approach to model design and construction provides a flexible framework in which to focus the multidisciplinary research and operational efforts needed to facilitate the development, selection, and application of the most robust distributed modelling methods. A variety of modular approaches have been developed, but with little consideration for compatibility among systems and concepts. Several systems are proprietary, limiting any user interaction. The US Geological Survey modular modelling system (MMS) is a modular modelling framework that uses an open source software approach to enable all members of the scientific community to address collaboratively the many complex issues associated with the design,...
Sensitivity of an intermediate ocean-atmosphere coupled model of the tropical Pacific to its oceanic vertical structure
Disagreement between predictions of the future behavior of the Arctic Oscillation as simulated in two different climate models: Implications for global warming
Integration of remotely sensed and model data to provide the spatial information basis for sustainable landuse