Cargando…
Detecting the impact of land cover change on observed rainfall
Analysis of observational data to pinpoint impact of land cover change on local rainfall is difficult due to multiple environmental factors that cannot be strictly controlled. In this study we use a statistical approach to identify the relationship between removal of tree cover and rainfall with dat...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715068/ https://www.ncbi.nlm.nih.gov/pubmed/31523501 http://dx.doi.org/10.7717/peerj.7523 |
_version_ | 1783447174232670208 |
---|---|
author | Liang, Chun Xia van Ogtrop, Floris F. Vervoort, R. Willem |
author_facet | Liang, Chun Xia van Ogtrop, Floris F. Vervoort, R. Willem |
author_sort | Liang, Chun Xia |
collection | PubMed |
description | Analysis of observational data to pinpoint impact of land cover change on local rainfall is difficult due to multiple environmental factors that cannot be strictly controlled. In this study we use a statistical approach to identify the relationship between removal of tree cover and rainfall with data from best available sources for two large areas in Australia. Gridded rainfall data between 1979 and 2015 was used for the areas, while large scale (exogenous) effects were represented by mean rainfall across a much larger area and climatic indicators, such as Southern Oscillation Index and Indian Ocean Dipole. Both generalised additive modelling and step trend tests were used for the analysis. For a region in south central Queensland, the reported change in tree clearing between 2002–2005 did not result in strong statistically significant precipitation changes. On the other hand, results from a bushfire affected region on the border of New South Wales and Victoria suggest significant changes in the rainfall due to changes in tree cover. This indicates the method works better when an abrupt change in the data can be clearly identified. The results from the step trend test also mainly identified a positive relationship between the tree cover and the rainfall at p < 0.1 at the NSW/Victoria region. High rainfall variability and possible regrowth could have impacted the results in the Queensland region. |
format | Online Article Text |
id | pubmed-6715068 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67150682019-09-13 Detecting the impact of land cover change on observed rainfall Liang, Chun Xia van Ogtrop, Floris F. Vervoort, R. Willem PeerJ Statistics Analysis of observational data to pinpoint impact of land cover change on local rainfall is difficult due to multiple environmental factors that cannot be strictly controlled. In this study we use a statistical approach to identify the relationship between removal of tree cover and rainfall with data from best available sources for two large areas in Australia. Gridded rainfall data between 1979 and 2015 was used for the areas, while large scale (exogenous) effects were represented by mean rainfall across a much larger area and climatic indicators, such as Southern Oscillation Index and Indian Ocean Dipole. Both generalised additive modelling and step trend tests were used for the analysis. For a region in south central Queensland, the reported change in tree clearing between 2002–2005 did not result in strong statistically significant precipitation changes. On the other hand, results from a bushfire affected region on the border of New South Wales and Victoria suggest significant changes in the rainfall due to changes in tree cover. This indicates the method works better when an abrupt change in the data can be clearly identified. The results from the step trend test also mainly identified a positive relationship between the tree cover and the rainfall at p < 0.1 at the NSW/Victoria region. High rainfall variability and possible regrowth could have impacted the results in the Queensland region. PeerJ Inc. 2019-08-26 /pmc/articles/PMC6715068/ /pubmed/31523501 http://dx.doi.org/10.7717/peerj.7523 Text en ©2019 Liang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Statistics Liang, Chun Xia van Ogtrop, Floris F. Vervoort, R. Willem Detecting the impact of land cover change on observed rainfall |
title | Detecting the impact of land cover change on observed rainfall |
title_full | Detecting the impact of land cover change on observed rainfall |
title_fullStr | Detecting the impact of land cover change on observed rainfall |
title_full_unstemmed | Detecting the impact of land cover change on observed rainfall |
title_short | Detecting the impact of land cover change on observed rainfall |
title_sort | detecting the impact of land cover change on observed rainfall |
topic | Statistics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715068/ https://www.ncbi.nlm.nih.gov/pubmed/31523501 http://dx.doi.org/10.7717/peerj.7523 |
work_keys_str_mv | AT liangchunxia detectingtheimpactoflandcoverchangeonobservedrainfall AT vanogtropflorisf detectingtheimpactoflandcoverchangeonobservedrainfall AT vervoortrwillem detectingtheimpactoflandcoverchangeonobservedrainfall |