The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018
ABSTRACTStandardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), traditionally derived at a monthly scale, are widely used drought indices. To overcome temporal-resolution limitations, we have previously developed and published a well-validated daily SPI...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-07-01
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| Series: | Big Earth Data |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2022.2148331 |
| _version_ | 1797506281386278912 |
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| author | Rongrong Zhang Virgílio A. Bento Junyu Qi Feng Xu Jianjun Wu Jianxiu Qiu Jianwei Li Wei Shui Qianfeng Wang |
| author_facet | Rongrong Zhang Virgílio A. Bento Junyu Qi Feng Xu Jianjun Wu Jianxiu Qiu Jianwei Li Wei Shui Qianfeng Wang |
| author_sort | Rongrong Zhang |
| collection | DOAJ |
| description | ABSTRACTStandardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), traditionally derived at a monthly scale, are widely used drought indices. To overcome temporal-resolution limitations, we have previously developed and published a well-validated daily SPI/SPEI in situ dataset. Although having a high temporal resolution, this in situ dataset presents low spatial resolution due to the scarcity of stations. Therefore, based on the China Meteorological Forcing Dataset, which is composed of data from more than 1,000 ground-based observation sites and multiple remote sensing grid meteorological dataset, we present the first high spatiotemporal-resolution daily SPI/SPEI raster datasets over China. It spans from 1979 to 2018, with a spatial resolution of 0.1° × 0.1°, a temporal resolution of 1-day, and the timescales of 30-, 90-, and 360-days. Results show that the spatial distributions of drought event characteristics detected by the daily SPI/SPEI are consistent with the monthly SPI/SPEI. The correlation between the daily value of the 12-month scale and the monthly value of SPI/SPEI is the strongest, with linear correlation, Nash-Sutcliffe coefficient, and normalized root mean square error of 0.98, 0.97, and 0.04, respectively. The daily SPI/SPEI is shown to be more sensitive to flash drought than the monthly SPI/SPEI. Our improved SPI/SPEI shows high accuracy and credibility, presenting enhanced results when compared to the monthly SPI/SPEI. The total data volume is up to 150 GB, compressed to 91 GB in Network Common Data Form (NetCDF). It can be available from Figshare (https://doi.org/10.6084/m9.figshare.c.5823533) and ScienceDB (https://doi.org/10.57760/sciencedb.j00076.00103). |
| first_indexed | 2024-03-10T04:30:27Z |
| format | Article |
| id | doaj.art-0a274cea772a4f59849fcaa95d8e62ca |
| institution | Directory Open Access Journal |
| issn | 2096-4471 2574-5417 |
| language | English |
| last_indexed | 2024-03-10T04:30:27Z |
| publishDate | 2023-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Big Earth Data |
| spelling | doaj.art-0a274cea772a4f59849fcaa95d8e62ca2023-11-23T04:16:29ZengTaylor & Francis GroupBig Earth Data2096-44712574-54172023-07-017386088510.1080/20964471.2022.2148331The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018Rongrong Zhang0Virgílio A. Bento1Junyu Qi2Feng Xu3Jianjun Wu4Jianxiu Qiu5Jianwei Li6Wei Shui7Qianfeng Wang8Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection/College of Environmental & Safety Engineering, Fuzhou University, Fuzhou, ChinaInstituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Lisboa, PortugalEarth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USAFujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection/College of Environmental & Safety Engineering, Fuzhou University, Fuzhou, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology/Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaGuangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, ChinaCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, ChinaFujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection/College of Environmental & Safety Engineering, Fuzhou University, Fuzhou, ChinaFujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection/College of Environmental & Safety Engineering, Fuzhou University, Fuzhou, ChinaABSTRACTStandardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), traditionally derived at a monthly scale, are widely used drought indices. To overcome temporal-resolution limitations, we have previously developed and published a well-validated daily SPI/SPEI in situ dataset. Although having a high temporal resolution, this in situ dataset presents low spatial resolution due to the scarcity of stations. Therefore, based on the China Meteorological Forcing Dataset, which is composed of data from more than 1,000 ground-based observation sites and multiple remote sensing grid meteorological dataset, we present the first high spatiotemporal-resolution daily SPI/SPEI raster datasets over China. It spans from 1979 to 2018, with a spatial resolution of 0.1° × 0.1°, a temporal resolution of 1-day, and the timescales of 30-, 90-, and 360-days. Results show that the spatial distributions of drought event characteristics detected by the daily SPI/SPEI are consistent with the monthly SPI/SPEI. The correlation between the daily value of the 12-month scale and the monthly value of SPI/SPEI is the strongest, with linear correlation, Nash-Sutcliffe coefficient, and normalized root mean square error of 0.98, 0.97, and 0.04, respectively. The daily SPI/SPEI is shown to be more sensitive to flash drought than the monthly SPI/SPEI. Our improved SPI/SPEI shows high accuracy and credibility, presenting enhanced results when compared to the monthly SPI/SPEI. The total data volume is up to 150 GB, compressed to 91 GB in Network Common Data Form (NetCDF). It can be available from Figshare (https://doi.org/10.6084/m9.figshare.c.5823533) and ScienceDB (https://doi.org/10.57760/sciencedb.j00076.00103).https://www.tandfonline.com/doi/10.1080/20964471.2022.2148331Drought indexclimate changeclimatic disasterGeneralized Extreme Value |
| spellingShingle | Rongrong Zhang Virgílio A. Bento Junyu Qi Feng Xu Jianjun Wu Jianxiu Qiu Jianwei Li Wei Shui Qianfeng Wang The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018 Big Earth Data Drought index climate change climatic disaster Generalized Extreme Value |
| title | The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018 |
| title_full | The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018 |
| title_fullStr | The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018 |
| title_full_unstemmed | The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018 |
| title_short | The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018 |
| title_sort | first high spatial resolution multi scale daily spi and spei raster dataset for drought monitoring and evaluating over china from 1979 to 2018 |
| topic | Drought index climate change climatic disaster Generalized Extreme Value |
| url | https://www.tandfonline.com/doi/10.1080/20964471.2022.2148331 |
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