Modular Assessment of Rainfall-Runoff Models Toolbox - Matlab code for 47 conceptual hydrologic models
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Updated
Mar 1, 2024 - MATLAB
Modular Assessment of Rainfall-Runoff Models Toolbox - Matlab code for 47 conceptual hydrologic models
Hydrological Model Assessment and Development
GR4J rainfall runoff model implemented in Python
NASAaccess is R package that can generate gridded ascii tables of climate (CIMP5) and weather data (GPM, TRMM, GLDAS) needed to drive various hydrological models (e.g., SWAT, VIC, RHESSys, ..etc). The package assumes that users have already set up a registration account(s) with Earthdata login as well as authorizing NASA GESDISC data access. Ple…
Python implementation of the GR2M monthly rainfall runoff model
Python implementation of Tank Hydrological model by Sugawara and Funiyuki (1956)
Python implementation of the TUWmodel developed by Parajka et al. (2007)
Semi-distributed Rainfall-Runoff model, using Graph Neural Networks to model an entire watershed with around 500 catchments
hydrological models
River flow prediction based on rainfall-flow model and Kalman Filter
The purpose of this project is to investigate whether we can establish the effectiveness of natural flood management (NFM) interventions undertaken in the British town of Shipston-on-Stour during 2017 to 2020 from publicly available meteorological data and private data from the river gauge in Shipston.
Repository for the perceptual model interactive map
The tools calculates peak discharge flow of water using the required inputs. The inputs can be given as text inputs or a excel file with the rainfall intensity attached can also be uploaded.
This repository contains supporting code for the paper "Selecting a conceptual hydrological model using Bayes' factors computed with Replica Exchange Hamiltonian Monte Carlo" by Mingo et al.
This project explores the application of soil moisture signature (Branger et al., 2019; Araki et al., 2020) to enhance streamflow and soil moisture prediction in a rainfall-runoff model.
Completed for the "Laboratory of Computational Physics Mod. B" under the supervision of Professor Carlo Albert. The project utilizes Keras in TensorFlow for implementation.
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