Self-calibrating Palmer Drought Severity Index (CRU) 2010-2018
Data and Resources
This dataset has no data
Additional Info
| Field | Value |
|---|---|
| Source | https://app.mapx.org/static.html?views=MX-I9G6I-YINDG-BPAV5&zoomToViews=true#JAAc6 |
| Author | UNEP/GRID-Geneva |
| Maintainer | UNEP/GRID-Geneva |
| Last Updated | December 7, 2022, 08:15 (UTC) |
| Created | December 7, 2022, 08:15 (UTC) |
| GUID | MX-I9G6I-YINDG-BPAV5 |
| Issued | 2019-11-21 20:30:04 |
| Language | EN |
| Modified | 2022-02-04 15:02:38 |
| Publisher email | info@mapx.org |
| Publisher name | UNEP/GRID-Geneva |
| Theme | Web Map |
| data_type | geospatial |
| keywords_m49 | WLD |
| projects_description | NEAT+ Global |
| projects_id | MX-WJO-FOV-NNB-1BN-SZN |
| projects_title | NEAT+ Global |
| range_end_at_year | 2022 |
| range_start_at_year | 2010 |
| source_abstract | This layer shows the average values over the 2010 to 2018 decade of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901 to 2018. Original data were subset and processed using QGIS at UNEP/GRID-Geneva. The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. The dataset has been updated each year using newer versions of CRU TS input data, currently to the end of 2018 using a preliminary version of CRU TS 4.03 (0.5° resolution). Please read this document [https://crudata.uea.ac.uk/cru/data/drought/scpdsi.global2018.readme.txt] for more information. (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355). (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review. (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1) (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here). (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). Data were downloaded from CRUDATA/Drought [https://crudata.uea.ac.uk/cru/data/drought/] |
| source_title | Self-calibrating Palmer Drought Severity Index (CRU) 2010-2018 |
| spatial | WLD |