gudaleon Goto Github PK

ela-modeling

em-tutorials-docker

engage

Welcome to the the repository for ENGAGE, my PhD research project which uses a participatory modelling methodology to develop a catchment-scale sediment dynamics model. This model is based within the Python programming language and situated within the ArcGIS software suite.

envirem

:exclamation: This is a read-only mirror of the CRAN R package repository. envirem — Generation of ENVIREM Variables. Homepage: http://envirem.github.io Report bugs for this package: https://github.com/ptitle/envirem/issues

eriehackchlorodata

Chloro data

eriehackmvp1

MVP for Erie Hack water reporting app

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

fabm

The Framework for Aquatic Biogeochemical Models (FABM): a Fortran 2003 programming framework for biogeochemical models of marine and freshwater systems.

fate-and-transport-modeling-of-ecosystem-and-watershed

Part of the materials used in the graduate course on watershed-scale fate and transport modeling

fishdynr

R package of fisheries science related population dynamics models

fvcom-cmake

CMake based build system for FVCOM

fvcom-toolbox

A collection of Matlab post- and pre-processing tools for the Finite Volume Community Ocean Model (FVCOM)

fvcom-toolbox-1

Fork of the fvcom-toolbox (original available at https://github.com/GeoffCowles/fvcom-toolbox)

glm-source

GLM-Model

gltc-stats

R and matlab processing scripts for calculating statistics and loading, parsing, and plotting raw data from the global lake temperature collaboration (GLTC).

gnometools

Assorted libraries and scripts for working with the General NOAA Operational Modeling Environment

gohugo-theme-ananke

Ananke: A theme for Hugo Sites

heatfluxanalyzer

hifreqcyano

Analysis of high frequency cyanobacteria monitoring data

hugo-academic

The website designer for Hugo. Build and deploy a beautiful website in minutes :rocket:

hydat

R package to interface to Canadian Hydrometric Data (HYDAT) published by Water Survey of Canada

hydrolidar

A new algorithm and associated software tools are presented for the purpose of extracting watershed hydrography directly from light detection and ranging (LiDAR) data. LiDAR data are typified by high density point measurements of terrain. The current state of the science requires that terrain data be discretized into a regularly spaced raster grid of elevations before watershed and hydrologic analysis can be executed. Areas of high terrain variability or roughness can become smoothed over in the process, effectively removing potentially valuable information. Resulting hydrographic data sets (e.g. watershed boundaries and stream networks) are used extensively in environmental modeling systems but are flawed from the outset by the smoothing process used to convert LiDAR points into raster grids. The algorithm presented here employs a K-D tree data structure that facilitates rapid neighborhood searches within the LiDAR data cloud. This is a critical component given the extremely large size of typical LiDAR datasets (often in the millions to billions of points). An outlet based nearest neighbor tree uphill-climbing downhill-pruning (UCDP) methodology is then engaged to create a flow network through the point cloud. From this flow network, watershed boundaries, pits or sinks, upstream areas, and stream networks can all be derived. The methodology is encoded as a plug-in for the MapWindow GIS software and tested on a number of LiDAR datasets.

hydrologic-flashiness

Matlab code used to extract metrics related to storm events, such as peak flow runoff, hydrograph duration, volume-to-peak ratio

hydrology_class

R hydrology sample scripts: teaching material

hydromad

Hydrological Model Assessment and Development

hymod

HyMod Rainfall-Runoff Model

hymod-1

R implementation of the hydrological model HyMOD.

hypoxia_lake_erie

iitk_cs676a_computervision_courseproject

Weakly Supervised Learning for Object Counting with Convolutional Neural Networks

intro-to-netcdf

An introduction to netcdf files in R, python, and matlab