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

Han Wang, Yunqing Xuan Orcid Logo

Environmental Modelling & Software, Volume: 124, Start page: 104598

Swansea University Author: Yunqing Xuan Orcid Logo

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DOI (Published version): 10.1016/j.envsoft.2019.104598

Abstract

We present in this paper the development of a new, open-source MATLAB toolbox SRS-GDA that aims to providerandom spatial sampling of grid-based hydro-climatic datasets for environmental change studies. This toolboxaddresses the needs of quantifying how hydro-climatic responses, which are often drive...

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Published in: Environmental Modelling & Software
ISSN: 1364-8152
Published: Elsevier BV 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa52972
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spelling 2020-12-14T16:41:31.0508834 v2 52972 2019-12-05 SRS-GDA: A spatial random sampling toolbox for grid-based hydro-climatic data analysis in environmental change studies 3ece84458da360ff84fa95aa1c0c912b 0000-0003-2736-8625 Yunqing Xuan Yunqing Xuan true false 2019-12-05 CIVL We present in this paper the development of a new, open-source MATLAB toolbox SRS-GDA that aims to providerandom spatial sampling of grid-based hydro-climatic datasets for environmental change studies. This toolboxaddresses the needs of quantifying how hydro-climatic responses, which are often driven by grid-based forcingdatasets such as climate model projections, vary with location and scale. The toolbox can be used to carry outrandom spatial sampling of grid-based quantities with various constraints: shape, size, location, dominantorientation and resolution. A case study of a large dataset, the GEAR rainfall dataset is supplied to demonstratethe typical uses case of this toolbox. The provision of the toolbox for downloading together with the sample datais also presented. Journal Article Environmental Modelling & Software 124 104598 Elsevier BV 1364-8152 Spatial random sampling, Grid-based data analysis, Environment change, MATLAB toolbox, Open source software 1 2 2020 2020-02-01 10.1016/j.envsoft.2019.104598 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University 2020-12-14T16:41:31.0508834 2019-12-05T11:27:00.5996295 Professional Services ISS - Uncategorised Han Wang 1 Yunqing Xuan 0000-0003-2736-8625 2 52972__16200__a455cae3c0d146089f6bb295466f00ca.pdf wang2020.pdf 2020-01-06T13:22:18.0663310 Output 2077295 application/pdf Version of Record true 2020-01-06T00:00:00.0000000 This is an open access article distributed under the terms of a Creative Commons Attribution 4.0 (CC BY) license. true eng http://creativecommons.org/licenses/by/4.0/
title SRS-GDA: A spatial random sampling toolbox for grid-based hydro-climatic data analysis in environmental change studies
spellingShingle SRS-GDA: A spatial random sampling toolbox for grid-based hydro-climatic data analysis in environmental change studies
Yunqing Xuan
title_short SRS-GDA: A spatial random sampling toolbox for grid-based hydro-climatic data analysis in environmental change studies
title_full SRS-GDA: A spatial random sampling toolbox for grid-based hydro-climatic data analysis in environmental change studies
title_fullStr SRS-GDA: A spatial random sampling toolbox for grid-based hydro-climatic data analysis in environmental change studies
title_full_unstemmed SRS-GDA: A spatial random sampling toolbox for grid-based hydro-climatic data analysis in environmental change studies
title_sort SRS-GDA: A spatial random sampling toolbox for grid-based hydro-climatic data analysis in environmental change studies
author_id_str_mv 3ece84458da360ff84fa95aa1c0c912b
author_id_fullname_str_mv 3ece84458da360ff84fa95aa1c0c912b_***_Yunqing Xuan
author Yunqing Xuan
author2 Han Wang
Yunqing Xuan
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description We present in this paper the development of a new, open-source MATLAB toolbox SRS-GDA that aims to providerandom spatial sampling of grid-based hydro-climatic datasets for environmental change studies. This toolboxaddresses the needs of quantifying how hydro-climatic responses, which are often driven by grid-based forcingdatasets such as climate model projections, vary with location and scale. The toolbox can be used to carry outrandom spatial sampling of grid-based quantities with various constraints: shape, size, location, dominantorientation and resolution. A case study of a large dataset, the GEAR rainfall dataset is supplied to demonstratethe typical uses case of this toolbox. The provision of the toolbox for downloading together with the sample datais also presented.
published_date 2020-02-01T04:05:40Z
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score 11.017797

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