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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 Authors: HAN WANG, 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 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 dri...

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Published in: Environmental Modelling & Software
ISSN: 1364-8152 1873-6726
Published: Elsevier BV 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa52972
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spelling 2025-04-10T14:46:24.5138796 v2 52972 2019-12-05 SRS-GDA: A spatial random sampling toolbox for grid-based hydro-climatic data analysis in environmental change studies a346e806bdf9650d3ff6eda184d7dd38 HAN WANG HAN WANG true false 3ece84458da360ff84fa95aa1c0c912b 0000-0003-2736-8625 Yunqing Xuan Yunqing Xuan true false 2019-12-05 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. Journal Article Environmental Modelling & Software 124 104598 Elsevier BV 1364-8152 1873-6726 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 COLLEGE CODE Swansea University The authors would like to thank the Centre of Hydrology and Ecology (CEH), UK for providing the GEAR dataset to test the toolbox. The co-author Han Wang's PhD study is jointly sponsored by the two scholarships offered by the Chinese Scholarship Council (CSC), China and the College of Engineering, Swansea University, UK, which are both gratefully acknowledged. This study is supported by the UK-China Urban Flooding Programme Grant (REF: UUFRIP\10021) from the Royal Academy of Engineering, United Kingdom. 2025-04-10T14:46:24.5138796 2019-12-05T11:27:00.5996295 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering 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
HAN WANG
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 a346e806bdf9650d3ff6eda184d7dd38
3ece84458da360ff84fa95aa1c0c912b
author_id_fullname_str_mv a346e806bdf9650d3ff6eda184d7dd38_***_HAN WANG
3ece84458da360ff84fa95aa1c0c912b_***_Yunqing Xuan
author HAN WANG
Yunqing Xuan
author2 HAN WANG
Yunqing Xuan
format Journal article
container_title Environmental Modelling & Software
container_volume 124
container_start_page 104598
publishDate 2020
institution Swansea University
issn 1364-8152
1873-6726
doi_str_mv 10.1016/j.envsoft.2019.104598
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
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description 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.
published_date 2020-02-01T16:08:52Z
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score 11.08899

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