mdekauwe Goto Github PK

climateniche

R package for analysing the climatic niche of a species

climatevegetationdynamics_grangercausality

Source code for the publications on "a non-linear Granger-causality framework to investigate climate–vegetation dynamics", by Papagiannopoulou et al., GMD & ERL 2017

cmip5_1pctco2

cmip5_on_nci

For processing CMIP5 data on NCI

co2_x_drought_figure

competing_optimal_adjustments

Repository containing all the code to reproduce the results in Sabot et al. (2022): Predicting resilience through the lens of competing adjustments to vegetation function. Submitted.

coupled_a_gs_leaf_temp_transp

Iteratively solve leaf temperature, Ci, gs, An and transpiration

coupled_canopy

covid19

cru_percentile

cru_ts_v4_mat_map_ai

Calculatte MAT, MAP and an aridity index from CRU 0.5 degree data.

ctsm

Community Terrestrial Systems Model (includes the Community Land Model of CESM)

da_sink_limited_experiment

R code to replicate the figures and analyses presented in DA paper with Sink limited container volume experiment

dalec-grass

DALEC code for managed grasslands

dalec2

DALEC2 model and 4D-Var assimilation functions.

dalec_enkf

data

Data and code behind the stories and interactives at FiveThirtyEight

decoupling_fluxnet2015

desica

Plant hydraulic model

desom

Diagnostics of ecosystem-scale stomatal optimization models.

dgvmtools

R package for processing, analysing and visualising ouput from Dynamic Global vegetation Models (DGVMs)

dot_files

Collection of scripts to restore all my dot files, install unix packages, git, etc

drought_analysis

ed2

Ecosystem Demography Model

ef_activities

Hands-on activities associated with the Ecological Forecasting book and graduate class

egu22-short-course

Learn to run ESMValTool from a Jupyter Notebook

emcee

The Python ensemble sampling toolkit for affine-invariant MCMC

empirical_lsm

A framework for running empirical land surface models.

emulator_optimizer

Uses a neural network to create a model emulator, that can then be used to determine optimum parameters sets via a Bayesian MCMC approach

eoldas_release