Data &amp; Analytics

Identify all glaciers within or intersecting the target hydrological basin using the Randolph Glacier Inventory (RGI). This stage determines whether glacier modeling is warranted and produces the glacier list that drives all subsequent OGGM stages. The glacier inventory is the foundation of any OGGM simulation — errors here propagate through every downstream step.

> **Stage ID**: s9_postprocessing > **Pipeline order**: 9 of 9 > **Depends on**: s8_output_extraction

> **Stage ID**: s8_output_extraction > **Pipeline order**: 8 of 9 > **Depends on**: s7_execution

LISFLOODA

**Package**: `hydrocraft-lisflood` v1.0.0 **Model**: LISFLOOD (EC-JRC spatially distributed hydrological model) **Domain**: Large-scale rainfall-runoff, flood forecasting, water resources **Language**: Python (with Numba JIT-compiled soil loop and routing) **Last updated**: 2026-03-25 **Stats**: 4 tools | 5 skill documents | 18 diagnostic triplets | ~1,800 lines of validated Python **Validation status**: `test_case_validated` (LF_ETRS89_UseCase cold run) ---

Extract simulation results from HydroCNHS, export to CSV for archival, compute evaluation metrics against observed data, and generate validation plots. This is the final quality-assurance step before the model is considered operational.

Parse, validate, and analyze HexWatershed outputs to extract watershed characteristics, generate summary statistics, and verify results against expected ranges. This stage converts raw JSON outputs into usable CSV tables and computes derived metrics.

Convert basin-overlapping CMFD/MSWX-style NetCDF forcing into the daily CSV format used by this KI's Python HEC-HMS engine. The implemented converter, `tools/convert_forcing_to_hms.py`, extracts basin-average precipitation, temperature, and radiation, applies unit conversions, computes PET, and writes `basin_avg_forcing.csv` plus `basin_info.json`.

GemPyA

| Field | Value | |-------------------|----------------------------------------------------| | Package | hydrocraft-gempy-geological | | Version | 1.0.0 | | Target model | GemPy v3 (2024.1) — 3D Implicit Geological Modeler| | Domain | 3D structural geology, geophysics | | Language | Python 3.10–3.12 ...

GLMA

**Package**: `hydrocraft-glm-lake` v1.0.0 **Model**: GLM v3.3.3 + AED2 water quality library **Created by**: Jianyun Zhang Research Group, Hohai University **Last updated**: 2026-03-21 **Stats**: 16 tools | 12 skill documents | 30 diagnostic triplets | 7 error log entries | ~4,630 lines of validated Python **Validation status**: `production_validated` (Miyun Reservoir, 2001-2010) ---

datapythonrust

Scale EPIC from single-site to regional simulations covering states, counties, or arbitrary areas of interest. This stage uses Geo-EPIC's spatial modules to automatically generate inputs for hundreds to thousands of sites from remote sensing and national databases, run them in parallel, and aggregate results into spatial maps.

Parse EPIC output files (.ACY, .DGN, and others) into structured DataFrames, compute derived variables (above-ground biomass, stress indicators), generate summary statistics, and create visualization plots.

DayCent's primary output is a binary `.bin` file. Human-readable variables are extracted by running `DDlist100_rev491` against the binary plus a variable list. This stage turns the binary into a tidy CSV suitable for plotting and metric computation.

Parse DNDCv.CAN v9.6.0 output files, extract key variables (crop yield, N2O emissions, N leaching, SOC change, water balance), process daily output for temporal analysis, and create validation plots. This skill covers the structure and content of all major output files produced by the model.

Thin wrapper for the preserved detailed note: [s4_postprocess.md](s4_postprocess.md).

Extract, analyse, and validate CLM5 history file outputs. Convert NetCDF history files to analysis-friendly formats (CSV, JSON), compute summary statistics, and compare against observations or published reference values.

Extract CLASSIC netCDF output variables into CSV format for analysis, plotting, and comparison with observations. Compute summary diagnostics including carbon budget balance, energy closure, and water balance.

Parse CE-QUAL-W2 output files and generate visualizations. The signature output is the **curtain plot**: a 2D longitudinal-vertical cross-section showing temperature (or other variables) from upstream to dam at different depths.

> **Stage ID**: s9_output_analysis > **Pipeline order**: 9 of 10 > **Depends on**: s8_execution