Debris flow modeling data using EDDA model

Description:

This dataset contains the numerical simulation results and corresponding topographic data used in the study published in the paper: “Multi-task super-resolution deep learning overcomes computational bottlenecks in high-fidelity debris flow modeling”.

The data is stored in GeoTIFF format and organized into a hierarchical folder structure based on the location and resolution of the debris flow simulation results. The repository includes data for the primary study area (Lantau) at multiple resolutions and a separate, independent test area (C210) for model inference validation.

1. Study Area: Lantau (lantau_*/)

Data for the Lantau Island, used primarily for model training and validation. It includes simulations at a fine resolution (5m) and two coarser resolutions (15m, 30m). Each subfolder contains the following raster files:

• lantau_5m/(Fine-Grid Simulation Outputs):

  This directory stores the core simulation outputs at 5-meter resolution.

  • File Naming Convention: c_catchment_id_d_time.tif(Debris Flow Depth) and c_catchment_id_v_time.tif(Debris Flow Velocity).

  • Examples: c_25_d_0.0.tif(Depth at catchment 25, t=0.0s), c_43_v_1320.0.tif(Velocity at catchment 43, t=1320.0s).

• lantau_5m_mask/(Inundation Extent):

  Binary masks indicating the final inundation extent derived from the 5m simulations (values typically 0 for non-inundated and 1 for inundated cells).

• lantau_5m_topo/(Topographic Derivatives):

  Input features for the deep learning model derived from the 5m DEM.

  • Files: c_catchment_id_acc.tif(Flow Accumulation), c_catchment_id_ele.tif(Elevation), c_catchment_id_slp.tif(Slope), c_catchment_id_profile.tif(Profile Curvature), <code class="hyc-commo

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