@cbevins/behaveplus-radical v0.3.2

behaveplus-radical

This is the developmental repository for behaveplus-radical, an ES6 Javascript clean-room redesign and reimplementation of the mathematical models contained in the BehavePlus Wildland Fire Modeling System (v6).

Summary

behaveplus-radical is a wildland fire modeling engine that runs in Node.js or in a web browser. Its intended use if for the development of applications (like BehavePlus) whose audiences are fire behavior researchers, Fire Behavior Analysts, and other expert users.

behaveplus-radical implements the mathematical models enshrined in the BehavePlus Wildland Fire Modeling System, which was programmed by Systems for Environmental Management with funding and technical supervision provided by the USDA Forest Service. The original BehavePlus is written in C++ and runs only on Windows operating systems. Its latest stable release is Version 5, and the most recent developmental release is Version 6 Alpha Build 626 (2018).

The goal of behaveplus-radical is to implement all the features scheduled for BehavePlus Version 6.

Surface Fuel Model Subsystem

Features Implemented in Both behaveplus-radical and BehavePlus6

• Standard BEHAVE/FARSITE fuel descriptors (fuel models) per Albini, Anderson, Rothermel, and Scott & Burgan can be specified as either:
  • a key (code or number) to a standard fuel model, or
  • individual fuel parameters (aka 'fuel modeling').
• Chaparral fuels can be dynamically estimated from input parameters per Rothermel and Philpot.
• Palmetto-gallberry fuels can be dynamically estimated from input parameters per Hough and Albini.
• Western aspen fuels can be dynamically estimated from input parameters per Brown and Simmerman.
• Two fuel models may be specified for a single surface fire to derive a weighted estimate of fire behavior.

BehavePlus6 Features Not Yet Implemented in behaveplus-radical

• User-created standard BEHAVE-FARSITE fuel models can be stored in a personal fuel catalog.
• The 'expected value' surface fire behavior weightging method
• The fire containment module (CONTAIN)

behaveplus-radical Features Not Available in BehavePlus6

• A mixture of any two standard BEHAVE-FARSITE, dynamic chaparral, dynamic palmetto-gallberry, or dynamic western aspen fuel models may be used in weighted fire behavior.

Design

behaveplus-radical is a complete re-design of the core BehavePlus Fire Modeling System:

• its is written in EcmaScript 2015 Javascript instead of C++

• it can be used by Node.js or browser applications instead of an installed Windows app,

• it employs a directed acyclical graph to dynamically determine inputs and an optimized processing order based on the selected outputs and model configurations.