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LISFLOOD

Orchestrate end-to-end LISFLOOD hydrological simulations with settings XML and Python tooling.

1|Updated Mar 31, 2026
Search Tags:#workflow#diagnostics#preflight#gis#hydrology#lisflood#flood-forecasting
One-click install
npx skills add https://github.com/lzwei196/KISS---Knowledge-Infrastructure-for-Scientific-Simulation --skill lisflood
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: LISFLOOD
Source: https://github.com/lzwei196/KISS---Knowledge-Infrastructure-for-Scientific-Simulation/tree/main/models/LISFLOOD
Command: npx skills add https://github.com/lzwei196/KISS---Knowledge-Infrastructure-for-Scientific-Simulation --skill lisflood

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, xarray, netCDF4, pandas.

What problem does it solve?

LISFLOOD enables AI-assisted, end-to-end hydrological modeling by providing a complete workflow to ingest data, configure models, execute simulations, and parse outputs in a repeatable, auditable manner.

Core Features & Use Cases

  • End-to-end workflow: input preparation, domain setup, soil parameter conversion, forcing conversion, model execution, and post-processing.
  • Robust preflight and diagnostics: built-in checks and 18 diagnostic triplets to catch common errors before running.
  • Reproducible results: parameterized LISFLOOD settings and scripted tools for cold/warm starts and Monte Carlo experiments.
  • Use Case: Run a 1-year basin-scale flood forecast with warm-start capability and automated output validation.

Quick Start

Run LISFLOOD with a complete settings.xml to execute a cold-start spin-up and generate outputs.

Frequently Asked Questions about LISFLOOD

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate end-to-end flood forecasting workflows in GIS?▼

Hydrological simulations use a settings XML to configure model runs, applying preflight checks and 18 diagnostic triplets before execution. This automates data preparation, model runs, and post-processing for basin-scale flood forecasting.

How do I configure LISFLOOD settings for a cold-start spin-up simulation?▼

Cold-start spin-up simulations are configured using a complete settings XML file that defines input data paths, soil parameters, and forcing data. The workflow then executes the run and generates validated hydrological outputs.

Can I run Monte Carlo experiments with LISFLOOD hydrological models?▼

Monte Carlo experiments are supported using parameterized LISFLOOD settings and scripted Python tools for reproducible results. This enables varied forcing data, soil, and channel network scenario analysis.

What preflight checks and diagnostics are available for flood forecasting models?▼

Flood forecasting models include built-in preflight checks and 18 diagnostic triplets to catch common errors before execution. These validate strict input formats and ensure robust basin-scale water balance studies and scenario analysis.

Do I need xarray and netCDF4 to run basin-scale water balance studies?▼

Basin-scale water balance studies require numpy, xarray, netCDF4, and pandas dependencies for data preparation and post-processing. These Python libraries support forcing conversion, model execution, and output validation.

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