There are 3 repositories under rainfall-runoff-model topic.
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. Please refer to https://disc.gsfc.nasa.gov/data-access for further details. The package relies on 'curl' (https://curl.haxx.se/) commands and library to access and download data from NASA remote sensing servers. Since Mac users have curl as part of macOS, Windows users should make sure that their local machines have 'curl' installed properly. Creating the ".netrc" file at the user machine 'Home' directory and storing the user NASA GESDISC logging information in it is needed to execute the package commands. Instructions on creating the ".netrc" and ".urs_cookies" files can be accessed at https://wiki.earthdata.nasa.gov/display/EL/How+To+Access+Data+With+cURL+And+Wget.
Python implementation of Tank Hydrological model by Sugawara and Funiyuki (1956)
Semi-distributed Rainfall-Runoff model, using Graph Neural Networks to model an entire watershed with around 500 catchments
Python implementation of the TUWmodel developed by Parajka et al. (2007)
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.
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.
Repository for the perceptual model database and 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 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.