rainfall-runoff

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

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.

Physically-based distributed hydrological model that simulates water and energy balances at the catchment scale, and runoff generation and propagation through the river network

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

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

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

HydroEvents is an open-source Python package for rainfall–runoff event extraction and event-based hydrological analysis.

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.

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.

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

A compact, reproducible deep-learning pipeline that predicts daily discharge at a single gauge from gridded daily meteorological fields over the contributing catchment.