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Developed with performance and flexibility in mind, it supports both CPU and GPU computations and can handle diverse forcing conditions such as spatially distributed rainfall, satellite-derived precipitation, design storms, and hydrograph inputs.\n\nThe model is particularly suited for urban, peri-urban, and rural catchments in both gauged and data-scarce regions.\n\n---\n\n##  Key Features\n\n- **2D overland flow** via Local-Inertial Model or Cellular-Automata\n- **Fully distributed infiltration** with Green-Ampt or Darcy's law\n- **Groundwater recharge and shallow aquifer flow** using Darcy's law and 2D Boussinesq Dynamics\n- **Flexible rainfall input**: gauges, satellite, interpolated, synthetic hyetographs, rasters\n- **Snow accumulation and melt** with mass balance tracking\n- **Dynamic reservoir and dam-break modeling**\n- **Pollutant simulation**: 1 pollutant per simulation with build-up/wash-off dynamics\n- **Mass-conserving adaptive time stepping** (Courant-based)\n- **Output visualizations** in `.tif`, `.mp4`, `.csv`, `.pdf`, and `.png`\n\n---\n\n##  Installation \u0026 Dependencies\n\n### MATLAB Requirements:\n- MATLAB R2020a or newer\n- Required Toolboxes:\n  - Mapping Toolbox\n  - Image Processing Toolbox\n- Optimization Toolbox when `flag_smoothening = 1`\n  - Parallel Computing Toolbox (for GPU mode)\n\n### Recommended:\n- NVIDIA GPU with at least 2 GB VRAM and compute capability \u003e 3.5\n- 8+ GB RAM for CPU mode\n\n### External Tools:\n- **Microsoft Excel** (for parameter configuration). Alternatively, users can only edit files in /config for rapid parametrization without requiring filling parameters/inputs in the Excel spreadsheets\n- **QGIS, Google Earth Engine, or R** for raster preprocessing (DEM, LULC, soils, etc.)\n- **Documentation** fully available with examples in the [HydroPol2D - Docs](https://marcusnobrega-eng.github.io/HydroPol2D-docs/).\n\n##  Getting Started\n\n### 1 Clone the Repository\n\n```bash\ngit clone https://github.com/marcusnobrega-eng/HydroPol2D.git\ncd HydroPol2D\n```\nAlternatively, download the ZIP from GitHub and extract it to your desired directory.\n\n## 2. Open MATLAB and Navigate to the Model Folder\n```bash\ncd('path/to/your/model/folder')\n```\nReplace path/to/your/model/folder with the location where you cloned the repository.\n\n### 3. Verify Required Files\nEnsure the following are present in your working directory:\n\n- `HydroPol2D_V115.m` (main model script)\n- `/config/` folder (parameters and flags)\n- `/HydroPol2D_functions`\n- `/third_party/topotoolbox_lite/` (bundled terrain runtime)\n- `Input_Spreadsheets` (if excel version is used)\n\n### 4. Set Up MATLAB\nOpen MATLAB or directly go in your model folder and open the file `HydroPol2D_V115.m` that MATLAB will automatically path to the model folder.\n\n### 5. Configure the Model\n\nEdit inputs in:\n\n- `/config/` folder. In particular, the file `input_data_bypass_script.m`. In case your forcing or other inputs do not follow the folder structure of the model, you may change the filepaths by editing `input_paths_bypass.m` in the same folder.\n- Edit `HydroPol2D_V115.m` to select `run_mode` and, for Excel mode, the `General_Data.xlsx` file. HydroPol2D registers its own functions and bundled terrain runtime automatically.\n- Excel parameter files located in the `\\Input_Data_Sheets` if you are running the model under the `Excel` mode, defined in the `HydroPol2D_V115.m` file.\n\n### 6. Run the Model\n`HydroPol2D_V115.m`\n\nOptional application workflows, including the SLURM template and SNISB dam-case runners, are grouped in [`Applications`](Applications/README.md).\n\n## Example\nExample of a rain-on-the-grid simulation of a 1 in 50-year rainfall in an urban area with the influence of urban drainage - Sao Paulo, Brazil.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"Generical_Input_Files/Rain_on_the_grid.gif.gif\" width=\"600\"\u003e\n\u003c/p\u003e\n\n\u003cimg src=\"https://marcusnobrega-eng.github.io/profile//files/Rain_on_the_grid.gif\"\u003e\n\n## Example of a total dam-break collapse scenario in a city in Pernambuco, Northeast - Brazil.\n\n\u003cimg width=\"672\" height=\"1008\" alt=\"dam_break\" src=\"https://github.com/user-attachments/assets/adfcdfe4-66e7-42aa-9335-de0e6d56a729\" /\u003e\n\n## Example of Rainfall-Runoff event in a catchment near Palo Alto -CA.\n\nhttps://github.com/user-attachments/assets/739e39eb-18e8-4ec3-bee6-a7a647793774\n\n## Example of a continuous simulation of a catchment in Pune - India with spatially-varied rainfall from MSWEP for a period of approximately 22 days.\n\nhttps://github.com/user-attachments/assets/3ad69bb3-3e24-4df3-b5ff-584aafcc6296\n\n## Input Data Structure\n\nHydroPol2D requires the following input data files and parameters, which must be prepared and aligned before running the model:\n\n### Raster Inputs (.tif)\n- **Digital Elevation Model (DEM):** Defines terrain slope, flow direction, and is used for hydrologic routing and terrain preprocessing. Must be gap-filled and optionally smoothed. Elevation in meters.\n- **Land Use / Land Cover (LULC):** Used to assign surface properties (e.g., roughness, imperviousness, pollutant loading). Each class must be indexed and named consistently with the LULC parameter table.\n- **Soil Map:** Classifies soils spatially to define infiltration and water balance parameters. Must match indices in the soil parameter table.\n- **Depth to aquifer or Depth to Bedrock** Constrains the vadose zone depth and surface sub-surface interactions\n- **Albedo / Leaf Area Index** Constrain the model parametrization for snow melt and evapotranspiration calculations\n- **Initial Conditions of Surface Depth, Soil Moisture, and Pollutant Mass** Constrain the model to appropriate initial conditions prior to simulations\n- **Optional Gridded Maps:**\n  - Rainfall intensity maps (mm/hr)\n  - Potential Evapotranspiration (ETP)\n  - Actual Evapotranspiration (ETa)\n  - Snow Depth\n  - Groundwater Table Depth\n  - Subgrid river bathymetry and slope rasters\n\nAll rasters must be aligned spatially (same extent, resolution, and coordinate reference, preferably EPSG:3857) or with an Equal Area projection.\n\n###  Parameter Tables (Excel Sheets)\n- **LULC Parameter Table:** Each land use class must include:\n  - Manning's `n` roughness coefficient\n  - Initial-abstraction `h0` and initial surface-water depth `d0` in mm\n  - Representative rooting depth in m\n  - Land-cover coefficient `Kc`, which scales internally calculated reference ET to potential soil ET. Ponded open water evaporates separately at `Ep`.\n  - Impervious index (0 or 1)\n  - Water-quality coefficients: `C1` (maximum buildup, kg ha^-1), `C2` (buildup coefficient, d^-1), `C3` (mass-based washoff coefficient), and `C4` (washoff exponent). These coefficients are pollutant- and site-specific; the generic template leaves buildup and washoff disabled.\n  - Per-LULC snow albedo, emissivity, phase thresholds, density bounds, and process-rate parameters when snow modeling is enabled.\n\nThe generic LULC table is a documented starting parameterization, not a substitute for local calibration. See [LULC default parameter basis](Input_Data_Sheets/LULC_Defaults.md) for the source basis and the scope of each field.\n\nFor the Neal (2012) channel-subgrid formulation, the LULC `n` column is the spatial floodplain roughness. Set the single in-bank/channel coefficient separately in `General_Data.xlsx` as `Manning`, or in script mode as `InputData_Bypass.general.Manning`. Observation-gauge data only define output locations and never alter either roughness field.\n\n- **Soil Parameter Table:** Each soil class must include:\n  - Vertical Saturated hydraulic conductivity $K_{\\mathrm{sat}}$\n  - van Genutchen $n$ and $\\alpha$\n  - Initial soil water content $\\theta_{\\mathrm{i}}$\n  - Saturated water content $\\theta_{\\mathrm{sat}}$\n  - Residual water content $\\theta_{\\mathrm{r}}$\n  - Horizontal Saturated hydraulic conductivity $K_{\\mathrm{sat,gw}}$\n\nPreprocessing must ensure that all rasters are aligned and projected.\n\n---\n\n\n##  Output Files\n\nHydroPol2D saves results in:\n- **Geospatial**: `.tif` rasters (depth, infiltration, pollutant, velocity, recharge, etc.) Alternatively, `.nc` files can be saved for time series of gridded data.\n- **Graphics**: `.png`, `.pdf` (hydrographs, profiles, maps)\n- **Animation**: `.mp4` (dynamic maps)\n- **Numerical**: `.csv` (time series, diagnostics)\n\n---\n\n##  Configuration Flags\n\nFlags are configured via Excel and define:\n- Simulation type (rainfall vs hydrograph)\n- Processing mode (CPU vs GPU)\n- Routing method (CA or Local Inertial)\n- Hydrologic modules (baseflow, ET, GW, snow, pollutant)\n- DEM preprocessing routines\n\nEnsure consistency among flags (e.g., only one rainfall mode active).\n\n---\n\n##  Performance \u0026 Scaling\n\n| Mode | Grid Size         | Notes                          |\n|------|-------------------|--------------------------------|\n| CPU  | \u003c 1 million cells | Single-core, slower            |\n| GPU  | \u003e 1 million cells | Faster, requires NVIDIA GPU    |\n\n- Adaptive time stepping based on Courant number (recommended: 0.3–0.7)\n\n---\n\n##  References\nCite HydroPol2D using:\n\n- Gomes Jr, M.N., Castro, M.A., Castillo, L.M., Sánchez, M.H., Giacomoni, M.H., de Paiva, R.C. and Bates, P.D., 2025. *[Spatio-temporal performance of 2D local inertial hydrodynamic models for urban drainage and dam-break applications](https://doi.org/10.1016/j.jhydrol.2025.134661).* Journal of Hydrology.\n\n- Gomes Jr, M. N., et al. (2023). *[HydroPol2D—Distributed hydrodynamic and water quality model: Challenges and opportunities in poorly-gauged catchments](https://doi.org/10.1016/j.jhydrol.2023.129982).* Journal of Hydrology, 625, 129982.\n\n- Gomes Jr, M.N., Jalihal, V., Castro, M. and Mendiondo, E.M., 2025. *[Exploring the impact of rainfall temporal distribution and critical durations on flood hazard modeling](https://doi.org/10.1007/s11069-025-07186-3).* Natural Hazards.\n\n- Gomes Jr, M. N., et al. (2024). *[Global optimization-based calibration algorithm for a 2D distributed hydrologic-hydrodynamic and water quality model](https://doi.org/10.1016/j.envsoft.2024.106128).* Environmental Modelling \u0026 Software, 179, 106128.\n\n- Rápalo, L.M., Gomes Jr, M.N. and Mendiondo, E.M., 2024. *[Developing an open-source flood forecasting system adapted to data-scarce regions: A digital twin coupled with hydrologic-hydrodynamic simulations](https://doi.org/10.1016/j.jhydrol.2024.131929).* Journal of Hydrology, 644, 131929.\n\n- Castro, M.D.A.R.A., Jr, M.N.G. and Mendiondo, E.M., 2025. *Probabilistic DAM break flood mapping via monte-carlo simulations using a 2D local-inertial model.*\n  *(DOI not available)*\n\n- Castro, M., Gomes Jr, M., Rápalo, L. and Mendiondo, E., 2025. *[Performance of Low-Complexity Hydrodynamic Models for Dam-Break Flood Mapping: Trade-offs between Full Momentum, Diffusive-Wave, and Local-Inertial Models](https://doi.org/10.22541/essoar.175242171.16927854/v1) (under review).\n\n- Sánchez, M.H., 2025. [A novel framework for flood vulnerability assessment and fuzzy-controlled reservoir optimization in data-scarce urban watersheds](https://teses.usp.br/teses/disponiveis/18/18138/tde-30072025-105642/publico/CORRIGIDO_Mateo_Hernandez_Sanchez.pdf) (Master's thesis, University of São Paulo).\n\n- Sanchez, M.H., Gomes Jr, M.N., Rápalo, L., Castro, M.D.A.R.A. and Mendiondo, E.M. 2026. *[Assessment of urban vulnerability to floods under current and future demographic and climate scenarios](https://doi.org/10.2139/ssrn.6381670).* SSRN (under review).\n\n- Castro, M.D.A.R.A., 2024. [Métodos determinísticos e probabilísticos para a avaliação do impacto de rompimentos de barragens via modelagem hidrodinâmica](https://teses.usp.br/teses/disponiveis/18/18138/tde-21032025-085649/publico/Dissertacao_versao_corrigida.pdf).\n\n\n- Gomes Jr., M. N. 2023. [Advances in open source hydroinformatics for flood modeling and disaster education](https://teses.usp.br/teses/disponiveis/18/18138/tde-18042024-142515/publico/TeseMarcusNobregaGomesJuniorVersaoCorrigidaCompressed.pdf) (Doctoral dissertation, Universidade de São Paulo).\n\n- Rápalo, L.M.C., 2024. [Open-source tools for flood risk assessment for multiple spatio-temporal scales under scenarios of change](https://teses.usp.br/teses/disponiveis/18/18138/tde-07102024-094249/publico/ThesisCastilloRapaloLuisMiguelCorrected.pdf) (Doctoral dissertation, Universidade de São Paulo).\n\n- Rápalo, L.M., Gomes Jr, M.N. and Mendiondo, E.M., 2025. *[Multiple levels of human instability due to urban overland flow within the 21st century: An urban Catchment study case in Brazil](https://doi.org/10.1016/j.ijdrr.2025.105931).* International Journal of Disaster Risk Reduction.\n\n- Sousa, M.R.D., Mendiondo, E.M. and Gomes, M.N., 2026. [Hydrological-hydrodynamic modeling of climate-induced urban flooding of design storms using HydroPol2D: a case study in São Carlos, Brazil](https://www.scielo.br/j/rbrh/a/KFQjyK4vxHPscnyCCRrBGjH/?format=pdf\u0026lang=en).* RBRH.\n\n---\n\n## Contributing \u0026 Support\n\nFor issues, contact:\n- **Dr. Marcus N. Gomes Jr.** – [mngomes@stanford.edu](mailto:mngomes@stanford.edu)\n\nRepository: [https://github.com/marcusnobrega-eng/HydroPol2D](https://github.com/marcusnobrega-eng/HydroPol2D)\n\n---\n\n![License](https://img.shields.io/badge/license-MIT-blue.svg)\n\nThis project is licensed under the **GNU General Public License, version 3**.\nThe bundled TopoToolbox runtime is documented in\n[`third_party/NOTICE.md`](third_party/NOTICE.md) and\n[`third_party/topotoolbox_lite/UPSTREAM.md`](third_party/topotoolbox_lite/UPSTREAM.md).\n\n\n# Short Course Link with 2-h classes + PPT material (Available upon request): marcusnobrega.engcivil@gmail.com\n\n## Acknowledgments\n\n- University of São Paulo — São Carlos School of Engineering\n- University of Texas at San Antonio — Civil and Environmental Engineering\n- Stanford University — Stanford Doerr School of Sustainability; Department of Earth System Science\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.1016/j.jhydrol.2025.134661","https://doi.org/10.1016/j.jhydrol.2023.129982","https://doi.org/10.1007/s11069-025-07186-3","https://doi.org/10.1016/j.envsoft.2024.106128","https://doi.org/10.1016/j.jhydrol.2024.131929","https://doi.org/10.22541/essoar.175242171.16927854/v1","https://doi.org/10.2139/ssrn.6381670","https://doi.org/10.1016/j.ijdrr.2025.105931"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/349073","html_url":"https://ost.ecosyste.ms/projects/349073"}