Software

Repository to investigate CWD spread using the first time of detection by county and comparing those results to what would be predicted based on deer movement distributions.

This code shows loading and formatting data, a series of functions to implement flow management constraints, a function to estimate parameters that drive daily discharge predictions, the optimization of the estimating function, and plotting code.

The purpose of this R software package is to easily download and process climate, anthropological, and environmental covariate data for specified areal units to use primarily in an integrated species distribution model. The package contains wrapper functions for the `geodata` R package and externally downloaded datasets.

The purpose of this R software package is to build and fit an integrated species distribution model. Specifically, this package loads species and covariate data, builds a species region, sets up code, parameters, and specification for a NIMBLE model, and processes and plots NIMBLE output. This framework allows the inclusion of any number of count, detection/non-detection, and presence...

The purpose of this R software package is to easily download and clean species data from the Global Biodiversity Information Facility (GBIF). This package relies on functions in the `rgif` package and contains wrappers to download and format data for the purposes of building an integrated species distribution model.

The Terrain Analysis Helper performs a suite of analyses on a series of digital elevation models (single-DEM analysis currently unavailable but planned). Most calculations from Fukunaga et al. (2019) are included, excluding d128-based fractals. From the MultiscaleDTM R package, the following metrics are included: vector ruggedness measure (VRM), topographic position index (TPI), profile...

Ursa is a python tool for the setup, management, and analysis of postfire debris-flow runout simulations. Ursa provides (1) a set of python functions that accomplish each phase of a hazard assessment workflow and (2) a Snakemake snakefile that uses the python functions to run the entire workflow. Ursa is intended as a flexible tool for a particular purpose. It is not intended as a...

This product contains Jupyter Notebooks demonstrating how to retrieve, process, and analyze USGS analysis ready data (ARD) using Python. The purpose of these examples is to introduce novice Python users to real-life earth science data workflows. The examples include using the data retrieval package to pull NWIS data, pulling NOAA NOS CO-OPs water level data to visualize hurricane Helene...

An introduction to version control for novices using Git and a guide for publishing USGS code. Please see https://cdi.code-pages.usgs.gov/usgs-git-and-software-release-course/ for a rendered version of this material.

This is a shorter tutorial, a quick guide, that shows users how to pull and view thumbnails and other images provided by the Landsat SpatioTemporal Asset Catalog (STAC).

The software release combines USGS streamgage data and NOAA precipitaion and storm track data to highlight where floods occurred, how long they lasted, and how storm intensity evolved. This kind of integrated data is critical for emergency response, risk communication, and long term planning.

This code is associated with Gerber et al. manuscript titled, "Monitoring wolverine (Gulo gulo) distribution across the western United States for regional and national assessment", authored by members of the Western Association of the Fish and Wildlife Agencies (WAFWA) Forest Carnivore Subcommittee