Good model performance?

For environmental scientists like hydrologists or ecologists, the performance of a model mostly refers to how well a simulation run mimics the modelled phenomenon, often evaluated by a broad range of measures comparing the simulated output to observed data. Increasing the model performance is then an ongoing process of improving the model by incorporating new or refining the existing implementation of environmental processes, possibly combined with using improved datasets at higher spatial and temporal resolutions. This, however, increases the computational burden of the simulations. Improving the computational performance of a model to efficiently support a range from stand-alone computers to HPC systems is typically not in the scope of an environmental scientist, while a reduced runtime would be beneficial for the entire modelling cycle.The LUE (https://zenodo.org/records/16792016) environmental modelling framework is a software package for building HPC-ready simulation models. The Python bindings provide domain scientists a large set of spatial operations for model building. All LUE operations are implemented in C++ using HPX (https://doi.org/10.5281/zenodo.598202), a library and runtime environment providing an optimal asynchronous execution of interdependent tasks on both shared-memory and distributed computing systems. Models constructed with LUE can therefore run on HPC systems without further modifications of the Python code and without explicit knowledge of programming HPC systems. In addition, the lue.pcraster Python sub-package provides an almost effortless transformation of existing PCRaster Python based models to LUE. In our presentation we showcase PCR-GLOBWB (https://doi.org/10.5194/gmd-11-2429-2018), a model simulating hydrology and water resources at a global scale, as an example of transforming an existing large scientific code base to LUE. We also demonstrate how efficiently the model now uses hardware ranging from one to thousands of CPUs, and therefore is prepared for global modelling studies at resolutions finer than 1 km.