Stars
A systematic approach to quantify the status of the terrestrial planetary boundaries based on the Dynamic Global Vegetation Model (DGVM) Lund-Potsdam-Jena managed Land (LPJmL). The supported planet…
Visualization and Analysis of CMIP6 Hydroclimatic Data
A collection of basic functions to facilitate the work with the DGVM LPJmL hosted at the Potsdam Institute for Climate Impact Research. It provides functions for running LPJmL, as well as reading, …
This is our development version of DNDCv.CAN. The model is under ongoing development. Please report issues.
Simple Python interface to the The Danish Meteorological Institute's (DMI) Open Data API.
Python library to train neural networks with a strong focus on hydrological applications.
🌻 pyet is a Python package to estimate reference and potential evaporation.
An R Package for downloading, preprocessing, and statistical downscaling of the European Centre for Medium-range Weather Forecasts ReAnalysis 5 (ERA5) family provided by the European Centre for Med…
A curated list of awesome ggplot2 tutorials, packages etc.
LandsatTS is an R package to facilitate retrieval, cleaning, cross-calibration, and phenological modeling of Landsat time-series data.
R client to geoBoundaries: A Political Administrative Boundaries Dataset -
Community Terrestrial Systems Model (includes the Community Land Model of CESM)
Code repository for the study "Overlooked risks and opportunities in groundwatersheds of the world’s protected areas." -- published in Nature Sustainability.
🎨 Visualisation toolbox for beautiful and publication-ready figures
PyAEZ is a python package consisted of many algorithms related to Agro-ecalogical zoning (AEZ) framework.
🌾🌽 Awesome lists about all crop simulation models
Create tidyverse methods for dealing with GEE image and imageCollections.
Community Water Model (CWatM) is a hydrological model simulating the water cycle daily at global and local levels, historically and into the future, maintained by IIASA’s Water Security group
🔦 Easily Extracting Information About Your Data in R