An R package for estimating river compound load using different methods. (July 2019)

• Record Type: Journal Article
• Authors: Nava, Veronica; Patelli, Martina; Rotiroti, Marco; Leoni, Barbara
• Is Part Of: Environmental modelling & software. Volume 117(2019)
• Page Start: 100
• Page End: 108
• Publication Date: 2019-07
• Subjects: Averaging method -- Load estimation -- R package -- Ratio estimator -- Regression method -- River; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.03.012
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The load quantification of solutes and suspended materials in rivers provides meaningful ecological information about watershed functionality. High-frequency measurements of flow are often available, whereas concentration data are commonly recorded at low frequencies. Different calculation methods have been developed by various authors to provide unbiased load estimation. We provide a new R package (RiverLoad ) that implements several of the most widely used load estimation algorithms. The package provides an easy-to-use tool to rapidly calculate the load for various compounds and to compare different methods. The package also supplies additional functions to easily organize and analyze the data. A bootstrapping was performed on two example datasets to illustrate the reliability of the methods at different sampling frequencies. TheRiverLoad package should make it easier to obtain load data and to compare different estimation algorithms. However, attention must be paid when selecting the method to avoid consistent error in the load estimation. Highlights: R package for estimating river compound load with different standardized methods. Outcomes of different methods can be easily compared. Open-source for reproducibility, openness, and future improvements. Additional functions to easily organize and analyze the data are supplied. Example datasets to demonstrate the reliability of the different procedures.