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- ParFlow**Package**: `hydrocraft-parflow` v1.2.0 **Model**: ParFlow v3.13+ (LLNL / Colorado School of Mines / Juelich) **Created by**: Jianyun Zhang Research Group, Hohai University **Last updated**: 2026-03-22 **Status**: **PRODUCTION VALIDATED** (Bengbu basin, real CMFD forcing, physically plausible discharge) **Stats**: 12 tools | 9 skill documents | 36 diagnostic triplets (12 validated) | 14 error log entries | 3,144 lines of validated Python ---Votes: 0GitHub stars: 200
- DocsDefine the 3D computational domain covering the basin in a UTM-projected Cartesian grid. This is the foundation for all subsequent stages -- every ParFlow file (subsurface, slopes, forcing, mask) must match the grid dimensions exactly.Votes: 0GitHub stars: 200
- DocsAssign spatially distributed hydraulic properties (permeability, porosity, van Genuchten parameters, Manning's n) to every grid cell. These control how water moves through the subsurface and over the surface.Votes: 0GitHub stars: 200
- DocsCompute slope_x and slope_y from DEM for overland flow routing. ParFlow uses these slopes with Manning's equation to route surface water.Votes: 0GitHub stars: 200
- DocsConfigure CLM 4.5 as the upper boundary condition. CLM provides evapotranspiration, snow, canopy interception, and radiation balance.Votes: 0GitHub stars: 200
- DocsConvert CMFD/MSWX forcing to ParFlow/CLM PFB format with correct units.Votes: 0GitHub stars: 200
- DocsSet the initial pressure head field and boundary conditions. The initial condition determines how quickly the simulation reaches physical equilibrium.Votes: 0GitHub stars: 200
- DocsConfigure the ParFlow solver (Richards equation, Newton-Krylov, preconditioner) and generate the run script.Votes: 0GitHub stars: 200
- DocsRun ParFlow, monitor convergence via kinsol.log, and validate output files.Votes: 0GitHub stars: 200
- DocsExtract hydrological variables from ParFlow PFB output and convert to standard formats.Votes: 0GitHub stars: 200
- PorePy| Field | Value | |------------------|--------------------------------------------------------------| | **Model** | PorePy v1.12.0 | | **Domain** | Fractured porous media simulation (subsurface flow, poromechanics) | | **Language** | Python 3.10+ | | **Build** | pip install (setuptools) ...Votes: 0GitHub stars: 200
- PyAEZ**Package**: hydrocraft-pyaez-crop v1.0.0 **Stats**: 5 tools | 6 skill documents | 15+ diagnostic triplets | 6 pipeline stagesVotes: 0GitHub stars: 200
- PyDeltaRCM**Package**: `pyDeltaRCM-ki` v1.0.0 **Model**: pyDeltaRCM v2.2.0 — Reduced-Complexity Delta Model **Domain**: Geomorphology / Delta Evolution / Sediment Transport **Authors**: Andrew J. Moodie, Jayaram Hariharan, Eric Barefoot, Paola Passalacqua **Paper**: Moodie et al. (2021), JOSS, 6(64), 3398 **Last updated**: 2026-03-26 **Stats**: 4 tools | 5 skill documents | 15+ diagnostic triplets **Validation status**: `tested` (default delta, 5 timesteps) ---Votes: 0GitHub stars: 200
- PyMT```yaml package: name: pymt-ki version: 1.0.0 target_model: PyMT (Python Modeling Toolkit) model_version: 1.3.3.dev0 domain: earth-surface-dynamics / model-coupling-framework language: python authors: [CSDMS team, mcflugen@gmail.com] license: MIT validation_status: tested ``` ---Votes: 0GitHub stars: 200
- PySWMM> PySWMM — Python Wrapper for EPA SWMM5 Stormwater Management Model > Domain: Urban hydrology / stormwater management > Engine: EPA SWMM 5.1.14–5.2.4 via swmm-toolkit > Language: Python 3.10–3.12 ---Votes: 0GitHub stars: 200
- Pywr**Package**: `pywr-knowledge-infrastructure` v1.0.0 **Model**: Pywr 1.30.0 (Python Water Resources Framework) **Domain**: Reservoir operations, water resources management, flood control, water supply **Role in HydroCraft**: Fills the reservoir operations gap — couples with VIC (inflow), CaMa-Flood (regulated routing), DSSAT (irrigation demand), DLBreach (dam-break trigger) ---Votes: 0GitHub stars: 200
- QUINCY**Package**: `kdt-quincy` v1.0.0 **Model**: QUINCY (Thum et al. 2019, GMD) **Domain**: Terrestrial biogeochemistry (coupled C-N-P cycling) **Created by**: KDT Auto-Dissection **Last updated**: 2026-03-30 **Stats**: 4 tools | 1 skill document | ~1,800 lines of validated Python **Validation status**: `real-data` (FI-Hyy Hyytiala, FLUXNET2015 monthly) ---Votes: 0GitHub stars: 200
- RAPID**Package**: hydrocraft-rapid-routing **Version**: 1.0.0 **Model**: RAPID (Routing Application for Parallel computatIon of Discharge) **Domain**: River network routing **Language**: Fortran 90 + PETSc **Tools**: 5 | **Skill Documents**: 6 | **Diagnostic Triplets**: 20 ---Votes: 0GitHub stars: 200
- Ki**Package**: hydrocraft-rapid-routing **Version**: 1.0.0 **Model**: RAPID (Routing Application for Parallel computatIon of Discharge) **Domain**: River network routing **Language**: Fortran 90 + PETSc **Tools**: 5 | **Skill Documents**: 6 | **Diagnostic Triplets**: 20 ---Votes: 0GitHub stars: 200
- RHESSys**Package:** `rhessys-ki` v1.0.0 **Model:** RHESSys (Regional Hydro-Ecologic Simulation System) v7.4 / 5.14.3 **Domain:** Hydrology, Ecohydrology, Biogeochemistry **Language:** C (501 source files) **Build:** GNU Make **Last Updated:** 2026-03-25 | Metric | Value | |--------|-------| | Tools | 4 | | Skill Documents | 5 | | Diagnostic Triplets | 20 | | Validation Status | HJ Andrews Watershed 8 | ---Votes: 0GitHub stars: 200
- ROMS**Target Model:** ROMS v4.1 (Regional Ocean Modeling System) **Domain:** Ocean hydrodynamics, coastal/estuarine modeling **Developers:** Rutgers University — H. Arango, A. Shchepetkin, J. Warner **License:** MIT/X style **Repository:** https://github.com/myroms/roms ---Votes: 0GitHub stars: 200
- RZWQM2python tools/s7_scenario_assembly/initialize_scenario.py \ /path/to/new_project bengbu_wheat <new_site_name> \ <start_date> <end_date> python tools/s8_execution/run_rzwqm2.py <scenario_dir> <binary_path> ``` **For mass/batch runs**, use `tools/s10_mass_generation/mass_project_generator.py` which automates this entire copy-then-update pipeline for a CSV of sites. ``` KISSPATH_HOME/RZWQM2/RZWQM2/linux/main_ryzen_patched ``` The binary is also copied into each scenario directory by the temp...Votes: 0GitHub stars: 200
- DocsRetrieve crop management parameters from five global geospatial datasets and write them into RZWQM2's `RZWQM.dat` management sections. This closes the gap where management events (planting dates, fertilizer amounts, harvest dates, irrigation mode) were previously inherited verbatim from template scenarios, enabling truly data-driven mass project generation.Votes: 0GitHub stars: 200
- Docs**Stage:** s1_site_config **Pipeline Order:** 1 **Depends On:** None **Tool:** `write_site_properties` ---Votes: 0GitHub stars: 200
- Docs**Stage:** s2_met_prep **Pipeline Order:** 2 **Depends On:** None (independent of site config, but needed before breakpoint generation) **Tools:** `generate_met_file`, `met_quality_check` ---Votes: 0GitHub stars: 200
- Docs**Stage:** s3_brk_generation **Pipeline Order:** 3 **Depends On:** s2_met_prep (requires a completed .met file with daily precipitation data) **Tool:** `create_breakpoint_file` ---Votes: 0GitHub stars: 200
- Docs**Stage:** s4_soil_setup **Pipeline Order:** 4 **Depends On:** s1_site_config **Tools:** `soil_texture_classification`, `pedotransfer_hydraulic`, `write_soil_properties` ---Votes: 0GitHub stars: 200
- Docs**Stage:** s5_node_discretization **Pipeline Order:** 5 **Depends On:** s4_soil_setup (requires horizon depths to be set in RZWQM.dat) **Tool:** `generate_nodes` ---Votes: 0GitHub stars: 200
- Docs**Stage:** s6_initial_conditions **Pipeline Order:** 6 **Depends On:** s4_soil_setup (horizon count and depths must match RZWQM.dat) **Tool:** `write_initial_conditions` ---Votes: 0GitHub stars: 200
- Docs**Stage:** s7_scenario_assembly **Pipeline Order:** 7 **Depends On:** s2_met_prep, s3_brk_generation, s4_soil_setup, s5_node_discretization, s6_initial_conditions **Tools:** `initialize_scenario`, `update_ipnames_paths`, `update_rzx_paths` ---Votes: 0GitHub stars: 200
- Docs**Stage:** s8_execution **Pipeline Order:** 8 **Depends On:** s7_scenario_assembly (complete, validated scenario directory) **Tool:** `run_rzwqm2` ---Votes: 0GitHub stars: 200
- Docs**Stage:** s9_result_parsing **Pipeline Order:** 9 **Depends On:** s8_execution (model must have completed successfully, producing output files) **Tools:** `parse_ana_output`, `parse_layer_output` ---Votes: 0GitHub stars: 200
- Raven**Package**: `hydrocraft-raven` v1.0.0 **Model**: Raven v4.1 (University of Waterloo, Prof. James Craig) **Created by**: Jianyun Zhang Research Group, Hohai University **Last updated**: 2026-03-21 **Stats**: 11 tools | 7 skill documents | 28 diagnostic triplets | 9 error log entries | ~4,500 lines of validated Python **Validation Status**: `production_validated` -- Full HydroCraft data run on Bengbu basin (118,358 km2, CMFD 224 cells, 2000-2005) ---Votes: 0GitHub stars: 200
- DocsCompare Raven output with VIC output for the same basin, and optionally couple Raven with CaMa-Flood for routing. The primary use case is quantifying **structural uncertainty** by comparing independent model results.Votes: 0GitHub stars: 200
- DocsChoose the right Raven emulation template for a basin. Wrong choice produces plausible but suboptimal results (e.g., no snow process in a snow basin). This stage determines the entire model structure.Votes: 0GitHub stars: 200
- DocsGenerate the Raven .rvh file defining subbasins and HRUs (Hydrologic Response Units). The .rvh file is the spatial foundation — it defines where and how water moves through the basin.Votes: 0GitHub stars: 200
- DocsBuild the Raven `.rvt` forcing file straight from a forcing source (CMFD, MSWX or NASA POWER) with correct units. **This is the single most critical stage in the entire Raven pipeline.** Raven explicitly states: "Raven ignores units and will not do units conversion." Every unit error produces a silent failure — the model runs fine with completely wrong results.Votes: 0GitHub stars: 200
- DocsRun multiple Raven model structures on the same basin with identical forcing and compare results. This quantifies **structural uncertainty** — how much hydrological predictions depend on model choice. This is Raven's unique capability within HydroCraft.Votes: 0GitHub stars: 200
- DocsOptimize model parameters using DDS (Dynamically Dimensioned Search) to match observed discharge. Calibrate ONLY the best-performing template from the ensemble comparison.Votes: 0GitHub stars: 200
- Ribasim**Package**: `hydrocraft-ribasim` v1.0.0 **Model**: Ribasim 2026.1.0-rc2 (Deltares) **Domain**: Water resources / regional surface water management **Created by**: HydroCraft Auto-Dissect **Last updated**: 2026-03-26 **Stats**: 4 tools | 5 skill documents | 18 diagnostic triplets **Validation status**: `prototype` ---Votes: 0GitHub stars: 200
- SFINCS**Package**: `hydrocraft-sfincs` v1.0.0 **Model**: SFINCS v2.x (Deltares) **Created by**: Jianyun Zhang Research Group, Hohai University **Last updated**: 2026-03-22 **Stats**: 9 tools | 8 skill documents | 28 diagnostic triplets | 12 error log entries | ~2,379 lines of validated Python **Validation**: Step 3 — Bengbu (Huai River) flood test (2026-03-22) — 384x455 cells, 100m, 397s, 6.99m max depth, CaMa-Flood cross-validated. Previous: Chaohe (2026-03-21) — 174x171 cells, 5.7s, 7.29m max dep...Votes: 0GitHub stars: 200
- DocsDefine the SFINCS computational grid from a basin shapefile or bounding box. This stage determines the spatial extent, resolution, and coordinate reference system for the entire simulation. All subsequent stages depend on the grid definition.Votes: 0GitHub stars: 200
- DocsBuild SFINCS topography (sfincs.dep), active cell mask (sfincs.msk), and index (sfincs.ind) files from a DEM. The quality of flood simulation depends critically on DEM accuracy and vertical datum consistency.Votes: 0GitHub stars: 200
- DocsGenerate spatially varying Manning's n roughness coefficient from land use/land cover data. Manning's n controls flow velocity and is the primary calibration parameter for SFINCS.Votes: 0GitHub stars: 200
- DocsPrepare all forcing inputs for SFINCS: precipitation (rainfall), river discharge boundaries (from CaMa-Flood), and optionally tidal/surge boundaries for coastal domains. This is the most error-prone stage due to **unit conversions**.Votes: 0GitHub stars: 200
- DocsAdd thin dams (levees, embankments), weirs, and drainage structures to the SFINCS model. This stage is OPTIONAL — only needed for domains with significant hydraulic infrastructure that affects flood routing.Votes: 0GitHub stars: 200
- DocsGenerate the main SFINCS configuration file (sfincs.inp) with all simulation parameters. The most critical parameter is the computational timestep `dt`, which must satisfy the CFL stability condition.Votes: 0GitHub stars: 200
- DocsExecute the SFINCS binary with preflight validation, log monitoring, and output verification. SFINCS is a Fortran binary that reads from the current working directory.Votes: 0GitHub stars: 200
- DocsExtract flood results from SFINCS NetCDF output, compute flood statistics, generate GeoTIFF rasters and publication-quality flood maps.Votes: 0GitHub stars: 200
- SHAWpython {KI}/s2_weather_prep/tools/convert_forcing_to_shaw.py --source nasa_power \ --lat 45.3 --lon -75.0 --start_year 2015 --end_year 2019 --mode daily --output site.wea python {KI}/s2_weather_prep/tools/convert_forcing_to_shaw.py --source cmfd \ --forcing_dir KISSPATH_DATA/forcing/Data_forcing_03hr_010deg \ --lat 32.43 --lon 115.6 --start_year 2010 --end_year 2010 --mode daily --output site.wea ``` | `--source` | Read by | Notes | |---|---|---| | `nasa_power` | shared loader (network, one r...Votes: 0GitHub stars: 200