The Simulation EnviRonment for Geomorphology, Hydrodynamics, and Ecohydrology in Integrated form (SERGHEI) is a multi-dimensional, multi-domain, and multi-physics model framework for environmental and landscape simulation, designed with an outlook towards Earth system modelling. At the core of SERGHEI's innovation is its performance-portable high-performance parallel-computing (HPC) implementation, built from scratch on the Kokkos portability layer, allowing SERGHEI to be deployed, in a performance-portable fashion, in graphics processing unit (GPU)-based heterogeneous systems. In this work, we explore combinations of MPI and Kokkos using OpenMP and CUDA backends. In this contribution, we introduce the SERGHEI model framework and present with detail its first operational module for solving shallow-water equations (SERGHEI-SWE) and its HPC implementation. This module is designed to be applicable to hydrological and environmental problems including flooding and runoff generation, with an outlook towards Earth system modelling. Its applicability is demonstrated by testing several well-known benchmarks and large-scale problems, for which SERGHEI-SWE achieves excellent results for the different types of shallow-water problems. Finally, SERGHEI-SWE scalability and performance portability is demonstrated and evaluated on several TOP500 HPC systems, with very good scaling in the range of over 20 000 CPUs and up to 256 state-of-the art GPUs.

Microtopography (MT) can govern runoff dynamics as a net result of local heterogeneities in the flow paths and ponding. This in turn controls the development of the surface water layer that connects and flows downslope. It is therefore important to understand which microtopographic features affect runoff generation dynamics and its macroscopic—hillslope scale—hydrological signatures (e.g., hydrographs, runoff and infiltration volumes). In this study, we numerically solve 2D overland flow from a single rain pulse on 1,460 idealized hillslopes with different slopes and sinusoidal microtopographies and different infiltration capacities. We assess hydrodynamic distributions, hydrographs and hydrological indices to assess the effects of MT and infiltration on the (local) hydrodynamic and (larger scale) hydrologic responses in terms of surface runoff regimes. The results show that MT enhances infiltration and that infiltration and runoff depend in a strong non‐linear way on slope and the properties of MT. Three regimes of influence of MT were identified: one in which MT plays a negligible role but there is a high sensitivity to the infiltration capacity curve, a second regime in which hydrological partitioning is highly sensitive to MT and the infiltration capacity curve, and a third regime in which MT increases infiltration, but the response is insensitive to particular features, and more affected by the average slopes. The regimes are the product of the interplay between small (MT) and large scale (slope) properties. Furthermore, the results suggest that hydrological signatures can be interpreted and explained by the spatiotemporal variation of surface connectivity.

An accurate assessment of the physical states of the Earth system is an essential component of many scientific, societal, and economical considerations. These assessments are becoming an increasingly challenging computational task since we aim to resolve models with high resolutions in space and time, to consider complex coupled partial differential equations, and to estimate uncertainties, which often requires many realizations. Machine learning methods are becoming a very popular method for the construction of surrogate models to address these computational issues. However, they also face major challenges in producing explainable, scalable, interpretable, and robust models. In this paper, we evaluate the perspectives of geoscience applications of physics-based machine learning, which combines physics-based and data-driven methods to overcome the limitations of each approach taken alone. Through three designated examples (from the fields of geothermal energy, geodynamics, and hydrology), we show that the non-intrusive reduced-basis method as a physics-based machine learning approach is able to produce highly precise surrogate models that are explainable, scalable, interpretable, and robust.