the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Jul 2026
GeoFlood (v1.0.0): Computational model for overland flooding
Abstract. This paper presents GeoFlood, a new open-source software package for solving the shallow-water equations (SWE) on a quadtree hierarchy of mapped, logically Cartesian grids managed by the parallel, adaptive library ForestClaw (Calhoun and Burstedde, 2017). The GeoFlood model is validated using standard benchmark tests from Neelz and Pender (2013) as well as the historical Malpasset dam failure. The benchmark test results are compared against those obtained from GeoClaw (Clawpack Development Team, 2020) and the software package HEC-RAS (Hydraulic Engineering Center - River Analysis System, Army Corps of Engineers) (Brunner, 2018). The Malpasset outburst flood results are compared with those presented in George (2011) (obtained from the GeoClaw software), model results from Hervouet and Petitjean (1999), and empirical data. The comparisons validate GeoFlood's capabilities for idealized benchmarks compared to other commonly used models as well as its ability to efficiently simulate highly dynamic floods in complex terrain, consistent with historical field data. Because it is massively parallel and scalable, GeoFlood may be a valuable tool for efficiently computing large-scale flooding problems at very high resolutions.
Status: open (until 15 Sep 2026)
- EC1: 'Comment on egusphere-2026-3967', Donghui Xu, 24 Jul 2026 reply
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RC1: 'Comment on egusphere-2026-3967', Anonymous Referee #1, 27 Jul 2026
reply
This manuscript presents the GeoFlood model, which may be useful for flood-inundation simulations over complex terrain. However, substantial revision is required before the manuscript can be considered for publication in Geoscientific Model Development.
(1) Novelty: The main concern is that the scientific and software novelty of GeoFlood is not yet sufficiently established. GeoFlood is built upon mature existing components, including GeoClaw solvers, ForestClaw, and p4est, and the manuscript describes the model as an integration of these libraries with additional AMR and visualization routines. It is unclear whether the principal contribution is a new numerical method, a new scalable AMR implementation, or primarily an integration of existing packages.
In the Introduction, the authors wrote "developing numerical models based on SWE remains a challenging problem with an active research community". However, there are tons of SWE-based flood models, such as LISFLOOD-FP, Telemac-2D, SERGHEI-SWE, Triton, etc. The authors should explicitly identify GeoFlood’s core advantage over existing flood models. This comparison should address the governing equations, numerical schemes, wetting-and-drying treatment, mesh structure and refinement strategy, parallelization approach, computational efficiency, and supported physical processes. If AMR is the principal innovation, the manuscript should demonstrate quantitatively why it offers a meaningful benefit over other established approaches.
(2) Parallelization: The manuscript emphasizes massively parallel and scalable flood simulation, but the reported performance assessment is CPU-based and limited to a single 48-core node. Given that many modern flood models increasingly exploit GPU acceleration, the computational competitiveness of GeoFlood is not yet demonstrated convincingly.
GPU support is not necessarily required for publication. However, if GPU acceleration is outside the scope of the present model, the authors should position GeoFlood as a CPU/MPI-based AMR framework rather than as a generally high-performance or state-of-the-art parallel flood model. The manuscript should also discuss the implications of this design choice for large-scale applications.
Furthermore, although Fig. 16 indicates higher parallel efficiency for GeoFlood than for GeoClaw, the absolute wall-clock-time reduction appears limited. A more detailed performance analysis would be valuable, including the computational costs of numerical updates, AMR regridding, inter-process communication, etc. Such profiling would clarify the principal bottlenecks and indicate which parts of the code are most difficult to parallelize or optimize.
(3) Test Cases: The four test cases primarily examine dry-bed inundation driven by prescribed inflow or dam-break release, wetting and drying, topographic effects, and AMR behavior. While these are relevant tests, they represent similar hydraulic scenarios and do not demonstrate a comprehensive flood-modeling framework.
The manuscript does not demonstrate rainfall forcing, evaporation, infiltration, stage boundary conditions, rainfall-runoff generation, urban features, or other potentially relevant hydrological and hydraulic processes. The authors should therefore either add benchmark or application cases for each additional capability that GeoFlood claims to support, or clearly define GeoFlood as a model for prescribed-flow and dam-break-type inundation, rather than implying that it is a general-purpose flood model.
(4) Quantitative Analysis: The comparisons with GeoClaw and HEC-RAS are mainly visual or qualitative, with results often described as “consistent” or “comparable.” More quantitative evaluation is needed. For each test case, the authors should report appropriate skill metrics, such as water-level and velocity errors, inundation-area agreement, mass-balance error, and computational cost. If AMR is the principal contribution of this study, a quantitative AMR convergence and efficiency analysis is also essential.
Citation: https://doi.org/10.5194/egusphere-2026-3967-RC1
Data sets
Datasets used in GeoFlood comparison to other models Brian Kyanjo https://doi.org/10.5281/zenodo.10897305
Model code and software
GeoFlood model Brian Kyanjo and Donna Calhoun https://doi.org/10.5281/zenodo.10929142
User Manual for running all codes used in the paper Brian Kyanjo https://drive.google.com/file/d/1O3QizHHNUrOUjw6Uw-G_2tLBPcZgT-PP/view?usp=sharing
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I encourage the authors to consider the comments from the previous review posted on NHESS Open Discuss: https://doi.org/10.5194/egusphere-2025-2173-RC1.