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MARRMoT

Run, calibrate, and validate MARRMoT conceptual rainfall-runoff models via Octave with unit-safe forcing conversion.

155|6|Updated Mar 31, 2026
Search Tags:#calibration#octave#hydrology#streamflow#rainfall-runoff#marrmot#gr4j
One-click install
npx skills add https://github.com/lzwei196/KISS-Knowledge-Infrastructure-for-Scientific-Simulation --skill marrmot-lzwei196
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: MARRMoT
Source: https://github.com/lzwei196/KISS-Knowledge-Infrastructure-for-Scientific-Simulation/tree/main/models/MARRMoT
Command: npx skills add https://github.com/lzwei196/KISS-Knowledge-Infrastructure-for-Scientific-Simulation --skill marrmot-lzwei196

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, xarray, matplotlib, oct2py, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve? Running the MARRMoT toolbox (47 conceptual rainfall-runoff model structures in MATLAB/Octave) from an agent workflow is error-prone: forcing data arrives in inconsistent units (mm/3h, kg/m2/s, Kelvin), PET must be pre-computed externally, and silent unit or column-order mistakes produce plausible-looking but wrong streamflow. This Skill encodes the full operational pipeline, unit-trap table, and diagnostic triplets so simulations, calibration, and validation execute correctly against the real model binary. ## Core Features & Use Cases - End-to-end pipeline tools: convert_forcing.py builds the [P, Ep, T] climate array in mm/d and deg C, convert_parameters.py maps soil/land data to parameter vectors, run_marrmot.py executes any of the 47 structures via Octave (with CMA-ES or Monte-Carlo calibration), and parse_output.py extracts Q, Ea, and storage to CSV with NSE/KGE/PBIAS metrics. - Diagnostic recovery: 20 symptom-to-diagnosis-to-remedy triplets in diagnostics/triplets.yaml cover silent unit conversions, solver non-convergence, S0 length mismatches, and water-balance issues, with a preflight_check.py that verifies the Octave binary, source tree, and data before any run. - Use Case: Calibrate GR4J (m_07_gr4j_4p_2s) on daily CMFD forcing for a catchment, then validate simulated streamflow against observed discharge converted from m3/s to mm/d, reporting NSE and PBIAS against cited Moriasi convention bands. ## Quick Start Run python preflight_check.py in this directory, then ask the agent to convert your forcing CSV with tools/convert_forcing.py and execute a GR4J run via tools/run_marrmot.py, scoring the output with tools/parse_output.py against your observed streamflow.

Frequently Asked Questions about MARRMoT

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

FAQPage Schema
How do I run a MARRMoT rainfall-runoff model from Python?▼

Use tools/run_marrmot.py with a forcing CSV containing [P, Ep, T] columns in mm/d and deg C, a model name like m_07_gr4j_4p_2s, and a theta parameter vector. The tool drives the real MARRMoT toolbox through an Octave subprocess and writes results to JSON.

How do I calibrate GR4J or HYMOD parameters in MARRMoT?▼

Run tools/run_marrmot.py with --calibrate --optimizer cmaes, which uses MARRMoT's built-in my_cmaes optimiser with an objective such as of_NSE or of_KGE. Use at least 5 IPOP restarts, since single mean-start runs get trapped in local optima.

What units does MARRMoT expect for precipitation, PET, and temperature?▼

MARRMoT expects precipitation and PET in mm/d and temperature in deg C, arranged as [P, Ep, T] columns, and performs no internal unit conversion. ERA5 kg/m2/s must be multiplied by 86400, CMFD mm/3h summed over 8 steps per day, and Kelvin reduced by 273.15.

Does MARRMoT compute potential evapotranspiration internally?▼

No, PET must be pre-computed externally with Hargreaves, Penman-Monteith, or Priestley-Taylor and supplied as the Ep column. Passing net radiation in W/m2 as PET silently makes evaporation exceed precipitation and drains the model stores.

Why is my MARRMoT simulated streamflow wildly wrong without any error?▼

Silent unit or format traps are the usual cause: wrong precipitation units, Kelvin temperature, swapped [P, T, Ep] columns, or delta_t set to 24 instead of 1 day. Check diagnostics/triplets.yaml first, which maps 20 known symptoms to diagnoses and remedies.

Can MARRMoT simulate snow-dominated or distributed catchments?▼

Only snow-capable structures (e.g. m_06, m_12, m_37) handle rain/snow partitioning; non-snow structures are structurally invalid in snow-dominated basins. All 47 structures are lumped 0-D models, so distributed or gridded routing is out of scope.

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