Design and development of a Python-based interface for processing massive data with the LOAD ESTimator (LOADEST). (January 2021)

• Record Type: Journal Article
• Authors: Gao, Jungang; White, Michael J.; Bieger, Katrin; Arnold, Jeffrey G.
• Is Part Of: Environmental modelling & software. Volume 135(2021)
• Publication Date: 2021-01
• Subjects: Water quality -- PyQt5 -- Graphic user interface -- Massive data processing -- LOADEST; 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.2020.104897
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: LOADEST is a program for estimating constituent loads in rivers and streams developed by the U.S. Geological Survey (USGS), but it does not have a Graphical User Interface (GUI) that facilitates processing of large amounts of data. Therefore, we present the LOAD ESTimation (LOADEST) Parallel Data Processing Interface (LPDPI). LPDPI is unique as it features an easy-to-use workflow for data download and water quality estimations for numerous stations and multiple constituents and is readily applicable to any station with both flow and water quality data available. LPDPI incorporates a parallel module for faster load estimation and can identify and fix errors that occur while running LOADEST by adjusting calibration and estimation data inputs. LPDPI also includes an extension to extract and filter LOADEST output to facilitate further data analysis and use of the data to calibrate hydrologic models. The tool is a standalone executable for Windows and can be readily used without any additional packages or software installation. Highlights: A Parallel Data Processing Interface (LPDPI) was developed for LOADEST. LPDPI can parallelly process data for numerous stations and multiple constituents. LPDPI uses a smart iteration design to fix errors that may occur when running LOADEST. LPDPI has the capability to extract and filter estimated results from LOADEST.