HydroDS: Data services in support of physically based, distributed hydrological models. (March 2020)

• Record Type: Journal Article
• Authors: Gichamo, Tseganeh Z.; Sazib, Nazmus S.; Tarboton, David G.; Dash, Pabitra
• Is Part Of: Environmental modelling & software. Volume 125(2020)
• Publication Date: 2020-03
• Subjects: HydroDS -- Web-based data services -- Distributed hydrologic modeling -- Geographic information systems -- Hydrologic data cyberinfrastructure; 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.2020.104623
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Physically based distributed hydrologic models require geospatial and time-series data that take considerable time and effort in processing them into model inputs. Tools that automate and speed up input processing facilitate the application of these models. In this study, we developed a set of web-based data services called HydroDS to provide hydrologic data processing 'software as a service.' HydroDS provides functions for processing watershed, terrain, canopy, climate, and soil data. The services are accessed through a Python client library that facilitates developing simple but effective data processing workflows with Python. Evaluations of HydroDS by setting up the Utah Energy Balance and TOPNET models for multiple headwater watersheds in the Colorado River basin show that HydroDS reduces the input preparation time compared to manual processing. It also removes the requirements for software installation and maintenance by the user, and the Python workflows enhance reproducibility of hydrologic data processing and tracking of provenance. Highlights: Web-based data services were developed for preparation of input data to selected distributed hydrologic models. Services are accessed through a Python client library and facilitate use in hydrologic data preprocessing workflows. Services reduce time for hydrologic model input preparation, enhance reproducibility, and enable tracking of data provenance.