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Manh-Hung Le's Projects

cimerg icon cimerg

This demonstration code is for ROK-US training #3

dataviz icon dataviz

A book covering the fundamentals of data visualization

delineator icon delineator

Fast, accurate watershed delineation using hybrid vector- and raster-based methods and data from MERIT-Hydro

geemap icon geemap

A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets.

geeup icon geeup

Simple CLI for Google Earth Engine Uploads

getspatialdata icon getspatialdata

An R package 📦 making it easy to query, preview, download and preprocess multiple kinds of spatial data 🛰 via R. All beta.

git-intro icon git-intro

uva library workshop on introduction to git and github

lisf icon lisf

Land Information System Framework

nasaaccess icon nasaaccess

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)

pytesmo icon pytesmo

python Toolbox for the Evaluation of Soil Moisture Observations

r-swat icon r-swat

This is an interactive web-based app for parallel parameter sensitivity, calibration, and uncertainty analysis with the Soil and Water Assessment Tool

r2point icon r2point

Extract timeseries for specific locations from a set of raster files

r2pol icon r2pol

Extract time series information for an area of interest from a set of raster files

smap icon smap

SMAP soil moisture downscaling

smap_enkf_swat icon smap_enkf_swat

SMAP data assimilation for hydrologic SWAT model streamflow simulation using Ensemble Kalman Filter

smapr icon smapr

An R package for acquisition and processing of NASA SMAP data

standard_precip icon standard_precip

Python implementation for calculating the Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI)

swatip icon swatip

This code introduces how to prepare forcing inputs data to create a SWAT project(s) step by step

tutorial icon tutorial

Tutorial on Bayesian tests for Machine Learning

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