globalchange  > 过去全球变化的重建
DOI: 10.1016/j.jhydrol.2019.04.037
WOS记录号: WOS:000476962800016
论文题名:
A new station-enabled multi-sensor integrated index for drought monitoring
作者: Jiao, Wenzhe1; Wang, Lixin1; Novick, Kimberly A.2; Chang, Qing3
通讯作者: Wang, Lixin
刊名: JOURNAL OF HYDROLOGY
ISSN: 0022-1694
EISSN: 1879-2707
出版年: 2019
卷: 574, 页码:169-180
语种: 英语
英文关键词: Climate change ; CONUS ; Drought monitoring ; GIIDI_station ; GWR ; PCA
WOS关键词: METEOROLOGICAL DROUGHT ; AGRICULTURAL DROUGHT ; VEGETATION ; CLIMATE ; REGRESSION ; MODIS
WOS学科分类: Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向: Engineering ; Geology ; Water Resources
英文摘要:

Remote sensing data are frequently incorporated into drought indices used widely by research and management communities to assess and diagnose current and historic drought events. The integrated drought indices combine multiple indicators and reflect drought conditions from a range of perspectives (i.e., hydrological, agricultural, meteorological). However, the success of most remote sensing based drought indices is constrained by geographic regions since their performance strongly depends on environmental factors such as land cover type, temperature, and soil moisture. To address this limitation, we propose a framework for a new integrated drought index that performs well across diverse climate regions. Our framework uses a geographically weighted regression model and principal component analysis to composite a range of vegetation and meteorological indices derived from multiple remote sensing platforms and in-situ drought indices developed from meteorological station data. Our new index, which we call the station-enabled Geographically Independent Integrated Drought Index (GIIDI_station), compared favorably with other common drought indices such as Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI). Using Pearson correlation analyses between remote sensing and in-situ drought indices during the growing season (April to October) from 2002 to 2011, we show that GlIDi_station had the best correlations with in-situ drought indices. Across the entire study region of the continental United States, the performance of GIIDI_station was not affected by common environmental factors such as precipitation, temperature, land cover and soil conditions. Taken together, our results suggest that GlIDLstation has considerable potential to improve our ability of monitoring drought at regional scales, provided local meteorological station data are available.


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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/141373
Appears in Collections:过去全球变化的重建

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作者单位: 1.IUPUI, Dept Earth Sci, Indianapolis, IN 46202 USA
2.Indiana Univ Bloomington, Sch Publ & Environm Affairs, Bloomington, IN 47405 USA
3.Univ Oklahoma, Ctr Spatial Anal, Dept Microbiol & Plant Biol, Norman, OK 73019 USA

Recommended Citation:
Jiao, Wenzhe,Wang, Lixin,Novick, Kimberly A.,et al. A new station-enabled multi-sensor integrated index for drought monitoring[J]. JOURNAL OF HYDROLOGY,2019-01-01,574:169-180
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