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hydro-forecast

Generate simulated runoff and flood indicators using hydrological models in Julia.

3|2|Updated Oct 24, 2025
Search Tags:#modeling#data-analysis#julia#hydrotools#hydroforecast#hydrology#flood
One-click install
npx skills add https://github.com/kongdd/Skills_for_Your_AI_Student --skill hydro-forecast
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hydro-forecast
Source: https://github.com/kongdd/Skills_for_Your_AI_Student/tree/main/skills/hydro-forecast
Command: npx skills add https://github.com/kongdd/Skills_for_Your_AI_Student --skill hydro-forecast

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables users to perform hydrological forecasting in Julia by orchestrating input data, model selection, and parameter management with HydroTools, reducing setup friction and enabling reproducible simulations.

Core Features & Use Cases

  • Supports multiple hydrological models (MarrMot, XAJ, TCN, LSTM, KAN) for flexible fitting to data.
  • Accepts structured input X and Y and optional JSON parameter files to configure complex simulations.
  • Produces outputs such as output, gof, info_flood, dat_flood, and evaluation for evaluation and visualization.

Quick Start

Prepare your data paths and parameters, then run the forecast workflow to obtain runoff and flood indicators.

Frequently Asked Questions about hydro-forecast

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

FAQPage Schema
How do I forecast hydrological flows and generate flood indicators using Julia?▼

To forecast hydrological flows in Julia, you provide structured input data (X, Y) and optional JSON parameter files to configure simulations. This generates simulated runoff and flood indicators like dat_flood and evaluation DataFrames.

What hydrological models are available for runoff simulation in Julia?▼

Hydrological forecasting in Julia supports multiple models including MarrMot, XAJ, TCN, LSTM, and KAN. This variety allows flexible fitting to different data regimes and specific flood event analysis.

Do I need to install Julia and HydroTools before running hydrological forecasting workflows?▼

Yes, hydrological forecasting requires a Julia environment with HydroTools installed. You must also provide explicit input data X, Y, and optional JSON parameter files to execute the simulations.

What outputs are produced when evaluating watershed-scale flood events?▼

Evaluating watershed-scale flood events produces named DataFrames including output, gof, info_flood, dat_flood, and evaluation. These outputs facilitate goodness-of-fit evaluation and flood event visualization.

Can I use deep learning models like LSTM for hydrological forecasting across different watershed scales?▼

Yes, hydrological forecasting supports deep learning models like LSTM and TCN alongside conceptual models like XAJ and MarrMot. This enables flexible fitting across various watershed scales and data regimes.

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