The Long-term Drought Visualizer (LTDV) is a web application built with RShiny and hosted on Amazon Web Services (AWS) EC2 instance. This app simulates soil moisture and is an exploratory dashboard for historical and future climate and soil moisture.
This application is a R based web application built with RShiny. The application serves as a graphical user interface for a mechanistic water-balance model, SOILWAT2, that has been proven in various publications. SOILWAT2 is a point based (lat, long) model that requires inputs of climate, soil texture, and vegetation structure. The source code for this model can be found here.
The LTDV app requires users to input site location, soils, and vegetation. If soils and vegetation are unknown soils are grabbed from SSURGO via an API, and allometric equations estimation vegetation composition and stucture from climate.
Based upon site coordinates gridded climate data is extracted from the climatology lab. Historical daily data (gridMET, 1980 - current year) is stored on the server. Future daily climate data (MACAv2, 2020 - 2099) is grabbed at the time of simulation for seven general circulation models (GCMs) and two representative concentration pathways (RCPs).
Once these inputs are generated / gathered, SOILWAT2 is executed for each climate scenario. Descriptive summary figures of climate and soil moisture are generated and available for download.
The application is a R application built with the RShiny web framework and hosted on an Elastic Cloud Computing (EC2) instance. The app relies on the suite of Dryland Ecology's R packages for running SOILWAT2. Historical weather data is provided by gridMET and stored on the EC2 instance. Future weather data is grabbed from an OPeNDAP server. Soils data is grabbed using the soilDB R package.
This application is deployed via docker on an AWS EC2 instance with an amazon-linux OS.
The instance is deployed via the EC2_AmazonLinux_Docker.yml cloud formation script.
The app is deployed via dockerfile.