LOADEST

Knowledge graph centered on LOADEST with 14 nodes and 35 connections. Top connected: Beetles, Pekania pennanti, higher plants, Significant stream chemistry response to temperatu, Dendroctonus ponderosae.

Method synopsis

Machine learning approach using USGS LOADEST software to develop concentration time-series models by combining discharge and concentration data. Used to extrapolate long-term trends and fill data gaps.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — Significant stream chemistry response to temperature variations in a high-elevation mountain watershed (2020), Communications Earth & Environment. DOI: 10.1038/s43247-020-00039-w. Implementations in other papers (listed below) may differ.

1. Long-term data compilation
   Long-term low-frequency measurements (LTM) by USGS from November 2000 to November 2019, measured at the very outlet of the watershed. Recent high-frequency measurements from April to October and every week from November to March from 2016 to 2019 measured at Coal-II about 1/3 of the creek length upstream into the watershed from the outlet.
   Quantities: 20 years of USGS data (2000-2019), 4 years of high-frequency data (2016-2019)Duration: USGS: November 2000 to November 2019; High-frequency: 2016-2019Conditions: Continuous monitoring across seasons
   Equipment: USGS monitoring equipment

Typical Equipment

• LOADEST software

Output Measurements

• predicted solute concentrations
• temporal trends
• load estimates

Papers Using This Protocol (1)

Species Studied With This Protocol

Places Where Used