Leistert et al. (2026) Accumulation-based Runoff and Pluvial Flood Estimation Tool (AccRo v.1.0)

Identification

• Journal: Geoscientific model development
• Year: 2026
• Date: 2026-03-10
• Authors: Hannes Leistert, Andreas Hänsler, Max Schmit, Andreas Steinbrich, M. Weiler
• DOI: 10.5194/gmd-19-2023-2026

Research Groups

Hydrology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany

Short Summary

This paper introduces AccRo (Accumulation-based Runoff and Flooding), a computationally efficient model designed to estimate maximum inundation depth, flow velocity, and specific discharge for pluvial flood events at larger spatial scales. The model demonstrates high accuracy in reproducing analytical solutions for design cases and closely matches the results of state-of-the-art 2D hydrodynamic models for real-world scenarios, offering a robust alternative for flood hazard assessment.

Objective

• To develop and validate AccRo (Accumulation-based Runoff and Flooding), a computationally efficient, raster-based model that accurately estimates maximum inundation depth, maximum flow velocity, and maximum specific discharge of pluvial flood events at larger spatial scales, addressing the computational demands and real-time forecasting challenges of 2D hydrodynamic models.

Study Configuration

• Spatial Scale: Regional scales (10–100 km²), small catchments (e.g., 3 km²), and urban/suburban areas. Model resolution tested at 1 m x 1 m and 2 m x 2 m grid cells.
• Temporal Scale: Pluvial flood events with durations ranging from 60 minutes to 135 minutes (observed event). Focus on maximum event states, with hydrographs derived as a by-product. Designed for real-time forecasting applications.

Methodology and Data

• Models used:
  • AccRo v.1.0: Accumulation-based Runoff and Flooding model (developed in this study).
  • HydroAs version 6.2.2: Commercial 2D hydrodynamic model (for comparison).
  • RIM2D (Version Jan 2025): GPU-based 2D hydrodynamic model (for comparison).
  • RoGeR: Hydrological model used to calculate spatially and temporally distributed surface runoff (s) input for real-world scenarios.
  • richDEM (Python module): Used for flow accumulation functions (FAF) and sink-filling.
• Data sources:
  • Digital Elevation Models (DEMs) for terrain analysis and slope derivation.
  • Spatially explicit land-use or surface cover information for Strickler surface roughness (k) estimation.
  • Radar-based precipitation input (implied for surface runoff generation).
  • TIN (Triangulated Irregular Network) data for HydroAs, rasterized for comparison.

Main Results

• AccRo accurately reproduces analytical solutions for design cases (hillslope and channel scenarios), matching or closely approximating Gauckler-Manning-Strickler (GMS) calculations for maximum water depth (wmax), specific discharge (qmax), and flow velocity (vmax).
• For real-world catchment simulations (Riedgraben, 3 km²), AccRo shows high agreement with HydroAs and RIM2D in the spatial patterns of maximum inundation depths.
• Quantitatively, AccRo's wmax values are very similar to those from hydrodynamic models, with a slight tendency to underestimate.
• AccRo generally simulates higher qmax and vmax values compared to HydroAs and RIM2D, though the correlation between the two hydrodynamic models for these variables is also less strong.
• AccRo demonstrates superior numerical stability compared to 2D hydrodynamic models, which experienced stability issues in some design cases, particularly RIM2D.
• AccRo is computationally efficient, simulating an observed event for a 0.75 million grid cell catchment in less than 2 minutes on a standard CPU, comparable to GPU-based RIM2D and significantly faster than HydroAs (approximately 2 hours on a standard CPU).
• AccRo-derived hydrographs show an earlier rise and faster drop compared to hydraulic models, with peak time differences generally within 2 minutes for the observed event, but larger for design scenarios.

Contributions

• Development of AccRo v.1.0, a novel, computationally efficient, raster-based model for pluvial flood hazard assessment, capable of estimating maximum inundation depth, flow velocity, and specific discharge.
• Introduction of an improved iterative flow accumulation method that dynamically modifies the DEM based on accumulated water, leading to a more realistic representation of inundation extent compared to traditional GIS-based flow accumulation methods.
• Demonstrated numerical robustness and stability of AccRo, making it suitable for complex cases and fine spatial resolutions where 2D hydrodynamic models may encounter stability issues.
• Validation against analytical solutions and two state-of-the-art 2D hydrodynamic models, showing high agreement and demonstrating its suitability for operational use cases, including real-time forecasting and pluvial flood map generation.
• Provides a valuable tool for assessing pluvial flood hazards based on peak conditions, offering a balance between accuracy and computational performance.

Funding

• Bundesministerium für Forschung, Technologie und Raumfahrt (grant no. 02WEE1629A)
• University of Freiburg (for open-access publication)

Citation

@article{Leistert2026Accumulationbased,
  author = {Leistert, Hannes and Hänsler, Andreas and Schmit, Max and Steinbrich, Andreas and Weiler, M.},
  title = {Accumulation-based Runoff and Pluvial Flood Estimation Tool (AccRo v.1.0)},
  journal = {Geoscientific model development},
  year = {2026},
  doi = {10.5194/gmd-19-2023-2026},
  url = {https://doi.org/10.5194/gmd-19-2023-2026}
}