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Flash flood simulations for an Egyptian city-mitigation measures and impact of infiltration Within this work, the impact of mitigation measures and infiltration on flash floods is investigated by using a 2D robust shallow water model including infiltration with the Green-Ampt model. The results show the combined effects of infiltration and mitigation measures as well as the effectiveness of bypass channels in addition to retention basins. Retention basins at appropriate locations could reduce the maximum water depth at critical locations by 23 %, while the additional implementation of drainage channels lead to a reduction of 75 %, considering also infiltration lead to a further reduction of 97 %. If infiltration was considered without mitigation measures, the peak water depth was reduced by 67 %. For an exceptional extreme event the measures lead to a reduction of 73 % at some locations, while at other locations the overflow from retention basins due to overstraining generated even higher inundations with an increase of 58 %.

This paper presents novel flux and source term treatments within a Godunov-type finite volume framework for predicting the depthaveraged shallow water flow and sediment transport with enhanced the accuracy and stability. The suspended load ratio is introduced to differentiate between the advection of the suspended load and 1 the advection of water. A modified Harten, Lax and van Leer Riemann solver with the contact wave restored (HLLC) is derived for the flux calculation based on the new wave pattern involving the suspended load ratio. The source term calculation is enhanced by means of a novel splitting-point implicit discretization. The slope effect is introduced by modifying the critical shear stress, with two treatments being discussed. The numerical scheme is tested in five examples that comprise both fixed and movable beds. The model predictions show good agreement with measurement, except for cases where local three-dimensional effects dominate. sediment transport; total load model; HLLC Riemann solver; finite-volume method; source term treatment Highlights 1. A second-order finite-volume method is presented for solving the total-load sediment transport 2. An improved HLLC Riemann solver is derived 3. An improved bed slope treatment is derived to account for density variation inside the cell 4. A novel implicit source term discretization is presented 5. The numerical model shows good agreement with measurement as long as the shallow flow assumptions are valid 2

A wavelet-based local mesh refinement (wLMR) strategy is designed to generate multiresolution and unstructured triangular meshes from real digital elevation model (DEM) data for efficient hydrological simulations at the catchment scale. The wLMR strategy is studied considering slope- and curvature-based refinement criteria to analyze DEM inputs: the slope-based criterion uses bed elevation data as input to the wLMR strategy, whereas the curvature-based criterion feeds the bed slope data into it. The performance of the wLMR meshes generated by these two criteria is compared for hydrological simulations; first, using three analytical tests with the systematic variation in topography types and then by reproducing laboratory- and real-scale case studies. The bed elevation on the wLMR meshes and their simulation results are compared relative to those achieved on the finest uniform mesh. Analytical tests show that the slope- and curvature-based criteria are equally effective with the wLMR strategy, and that it is easier to decide which criterion to take in relation to the (regular) shape of the topography. For the realistic case studies: (i) slope analysis provides a better metric to assess the correlation of a wLMR mesh to the fine uniform mesh and (ii) both criteria predict outlet hydrographs with a close predictive accuracy to that on the uniform mesh, but the curvature-based criterion is found to slightly better capture the channeling patterns of real DEM data.

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An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land-surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100m and finer). However, the frequency of these observations is very low, typically once or twice per season in Rocky Mountains, Colorado. Here, we present a machine learning framework based on Random Forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining fifteen different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination (R2) using our approach was 0.57 and the root mean squared error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R2 = 0.13, RMSE = 20 cm). We explored the relative importance of the input variables, and observed that at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables which characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points.

Mountainous watersheds supply a significant portion of the water resources used in regions downstream of these headwater catchments, particularly in arid and semi-arid regions. These watersheds are especially sensitive to the climate changes that are likely to impact the management of water resources. An example is the upper Colorado River watershed, which serves as the primary water source in the western United States (U.S.). However, the western U.S. has been experiencing water scarcity (Garrick et al., 2008), for example, low volumes at Lake Mead, Nevada have triggered for the first time a water shortage declaration (Fountain, 2021). Snow-dominated mountainous watersheds store water in the form of snowpack in the winter and release it by snowmelt in the summer. As a result, global warming could fundamentally alter the rain-snowfall ratio, shift early snowmelt, and

Abstract. 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.