At the core of many avalanche simulation tools, numerical kernels are utilized to solve flow model equations. Aside from trying to fit the models as best as possible to the current understanding of actual flow mechanisms, these kernels have to fulfill general mathematical requirements, such as convergence, stability and consistency. The precision of numerical solutions is limited and needs to be determined by appropriate uncertainty quantification approaches. It is also necessary to assess the impact of input variability propagating through the numerical kernel.
To allow kernel testing and uncertainty quantification, the AvaFrame framework provides a suite of test cases as well as analysis tools. This includes tests with known solutions usable to determine the kernel errors (ana1Tests) and idealized/real world topographies to estimate effects of varying simulation setups. By changing numerical settings, flow model setup or input data it is possible to show their effects on simulation results in a quantitative manner. It therefore allows us to relate input variations to the uncertainty in simulation results. Error and uncertainty quantification is done using modules for computing statistical measures (ana4Stats), indicators along an avalanche
path (ana3AIMEC) and various visualization routines.
