SRS-GDA: A spatial random sampling toolbox for grid-based hydro-climatic data analysis in environmental change studies. (February 2020)

• Record Type: Journal Article
• Authors: Wang, Han; Xuan, Yunqing
• Is Part Of: Environmental modelling & software. Volume 124(2020)
• Publication Date: 2020-02
• Subjects: Spatial random sampling -- Grid-based data analysis -- Environment change -- MATLAB toolbox -- Open source software; 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.2019.104598
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We present in this paper the development of a new, open-source MATLAB toolbox SRS-GDA that aims to provide random spatial sampling of grid-based hydro-climatic datasets for environmental change studies. This toolbox addresses the needs of quantifying how hydro-climatic responses, which are often driven by grid-based forcing datasets such as climate model projections, vary with location and scale. The toolbox can be used to carry out random spatial sampling of grid-based quantities with various constraints: shape, size, location, dominant orientation and resolution. A case study of a large dataset, the GEAR rainfall dataset is supplied to demonstrate the typical uses case of this toolbox. The provision of the toolbox for downloading together with the sample data is also presented. Highlights: An open-source MATLAB toolbox for spatial random sampling with various contrains: shape, size, location, dominant orientation and resolution. This toolbox addresses the needs of quantifying how hydro-climatic responses vary with location and scale. Direct applications in many environmental change studies where the spatial properties of grid-based datasets are the focus.