The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Big Earth Data Pub Date : 2023-01-03 DOI:10.1080/20964471.2022.2148331
Rongrong Zhang, Virgílio A. Bento, Junyu Qi, Feng Xu, Jianjun Wu, Jianxiu Qiu, Jianwei Li, Wei Shui, Qianfeng Wang
{"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","authors":"Rongrong Zhang, Virgílio A. Bento, Junyu Qi, Feng Xu, Jianjun Wu, Jianxiu Qiu, Jianwei Li, Wei Shui, Qianfeng Wang","doi":"10.1080/20964471.2022.2148331","DOIUrl":null,"url":null,"abstract":"ABSTRACT Standardized 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).","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Earth Data","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/20964471.2022.2148331","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 7

Abstract

ABSTRACT Standardized 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).
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
1979 - 2018年中国首个高空间分辨率多尺度日SPI和SPEI栅格数据集干旱监测与评价
标准化降水指数(SPI)和标准化降水蒸散指数(SPEI)是目前广泛应用的干旱指标,传统上以月为尺度推导。为了克服时间分辨率的限制,我们之前开发并发布了一个经过良好验证的每日SPI/SPEI原位数据集。虽然具有较高的时间分辨率,但由于站点的稀缺,该原位数据集的空间分辨率较低。基于中国气象强迫数据集(由1000多个地面观测点和多个遥感栅格气象数据集组成),首次构建了中国地区高时空分辨率的日SPI/SPEI栅格数据集。时间跨度为1979 ~ 2018年,空间分辨率为0.1°× 0.1°,时间分辨率为1天,时间尺度为30天、90天和360天。结果表明,日SPI/SPEI探测的干旱事件特征空间分布与月SPI/SPEI基本一致。12个月量表日值与SPI/SPEI月值相关性最强,呈线性相关,Nash-Sutcliffe系数和标准化均方根误差分别为0.98、0.97和0.04。日SPI/SPEI比月SPI/SPEI对突发性干旱更为敏感。我们改进的SPI/SPEI具有较高的准确性和可信度,与每月SPI/SPEI相比,结果有所增强。数据总量高达150gb, NetCDF (Network Common data Form)格式压缩为91gb。可以从Figshare (https://doi.org/10.6084/m9.figshare.c.5823533)和ScienceDB (https://doi.org/10.57760/sciencedb.j00076.00103)获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Big Earth Data
Big Earth Data Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
7.40
自引率
10.00%
发文量
60
审稿时长
10 weeks
期刊最新文献
Historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing spatio-temporal impact of on-shore wind in Japan Long-term (2013–2022) mapping of winter wheat in the North China Plain using Landsat data: classification with optimal zoning strategy Marginal land in China suitable for bioenergy crops under diverse socioeconomic and climate scenarios from 2020–2100 Towards seamless environmental prediction – development of Pan-Eurasian EXperiment (PEEX) modelling platform GEOSatDB: global civil earth observation satellite semantic database
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1