GR4J rainfall runoff model implemented in Python

Hydrological Model Assessment and Development

An implementation of the rainfall-runoff model SMART in Python

Python implementation of the GR2M monthly rainfall runoff model

ggRunoff: Visualisation of rainfall-runoff process lines using ggplot2 syntax. 利用ggplot2语法绘制洪水过程线

Open, agentic platform for hydrological modelling — an agentic runtime orchestrates data, model selection, training, runs & audit; LSTM for large-scale catchment streamflow & floods, SWMM for urban drainage & LID. Built on open data.

A C++ accelerator extension of the rainfall-runoff SMART for Python

A generic Netlogo Agent Based Model to simulate rainfall runoff and erosion in a watershed

GR4J Rainfall-Runoff Model with Automatic Calibration; using Deterministic Methods

Map-first, climate-informed flood hazard assessment for data-scarce basins in R: rainfall extreme value analysis (GEV), rainfall-runoff simulation, terrain-based flow routing and water-depth mapping in one reproducible pipeline, with a built-in stationary-vs-nonstationary test for changing rainfall extremes.

Completed for the "Laboratory of Computational Physics Mod. B" under the supervision of Professor Carlo Albert. The project utilizes Keras in TensorFlow for implementation.

Hydro_Mat a Free Software that contains Hydrological Models under Matlab

GR4J Rainfall Runoff Implemented in Fortran 90 with SCE-UA Optimisation

Flood Frequency Distribution (FFD) is free software to analyze flood and estimate Quantile for Different return Periods and Flood frequency relations

Machine Learning based rainfall-runoff prediction system using hydrological parameters for Dhanbad catchment analysis.

Reproducible HydroMT and Wflow SBM model for the Sylhet-Upper Meghna basin, Bangladesh, with MERIT Hydro hydrography and BWDB discharge gauges.

Hydrological modeling toolkit for Python: rainfall-runoff transformation, unit hydrographs, and watershed analysis.

Python for hydrology & watershed modeling automation — production-grade guides for DEM processing, flow routing, watershed delineation, and rainfall-runoff modeling.

Hydrological modeling framework with a GPU-accelerated solver for large-scale calibration across hundreds to thousands of parameter sets, uncertainty quantification, ensemble and realization analysis using multiple forcing or parameter scenarios, scenario exploration, and regional reanalysis.

End-to-end streamflow forecasting pipeline: real USGS + ERA5 data, Numba-JIT 5-parameter model, Differential-Evolution calibration, split-sample validation, Regional Sensitivity Analysis, GLUE uncertainty, and climate-change scenarios for the Russian River, California.