Arid
DOI10.1007/s11069-014-1499-3
Identification of long-term annual pattern of meteorological drought based on spatiotemporal methods: evaluation of different geostatistical approaches
Bayat, Bardia1; Nasseri, Mohsen1; Zahraie, Banafsheh2
通讯作者Bayat, Bardia
来源期刊NATURAL HAZARDS
ISSN0921-030X
EISSN1573-0840
出版年2015
卷号76期号:1页码:515-541
英文摘要

Estimation and identification of long-term meteorological drought pattern play an important role in regional water management and dry land agricultural practices in arid and semiarid climates. In this work, Standardized Precipitation Index (SPI) has been selected as the main criterion for evaluating the severity of meteorological drought events. The purpose of this paper was to produce meteorological drought occurrence probability maps for different SPI classes by spatiotemporal analysis. Several statistical methods known as non-geostatistical approaches (such as Thiessen polygons, inverse distance-weighted, and spline-based) and geostatistical approaches (such as different types of kriging and Bayesian maximum entropy (BME)) are available, which can be used for the purpose of this study. In this study, ordinary kriging (OK) as a classical geostatistical method and BME as a modern geostatistical method have been used. The case study of this research has been the Namak Lake Watershed located in the central part of Iran with an area of approximately 90,000 km(2). This basin includes regions with significantly different climatic conditions ranging from very dry to very wet. The results of the case study include spatial distribution of SPI for dry SPI classes (moderately, severely, and extremely dry classes) and wet SPI classes (moderately, severely, and extremely wet classes) which can be used to locate vulnerable areas against drought. The selected geostatistical methods have been compared based on leave-one-out cross-validation procedure and spatiotemporal distribution of SPI values. The results of cross-validation have shown the superiority of BME over OK. BME maps of probability of occurrence have also been more realistic than OK maps.


英文关键词Long-term drought pattern Geostatistical simulation Standardized Precipitation Index (SPI) Ordinary kriging (OK) Bayesian maximum entropy (BME)
类型Article
语种英语
国家Iran
收录类别SCI-E
WOS记录号WOS:000350326200027
WOS关键词STANDARDIZED PRECIPITATION INDEX ; BAYESIAN MAXIMUM-ENTROPY ; PIEZOMETRIC HEAD ; SPATIAL-ANALYSIS ; COVARIANCE ; RAINFALL ; SOIL ; SPI ; INTERPOLATION ; DISTRICT
WOS类目Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources
WOS研究方向Geology ; Meteorology & Atmospheric Sciences ; Water Resources
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/189311
作者单位1.Univ Tehran, Sch Civil Engn, Tehran, Iran;
2.Univ Tehran, Coll Engn, Sch Civil Engn, Ctr Excellence Infrastruct Engn & Management, Tehran, Iran
推荐引用方式
GB/T 7714
Bayat, Bardia,Nasseri, Mohsen,Zahraie, Banafsheh. Identification of long-term annual pattern of meteorological drought based on spatiotemporal methods: evaluation of different geostatistical approaches[J],2015,76(1):515-541.
APA Bayat, Bardia,Nasseri, Mohsen,&Zahraie, Banafsheh.(2015).Identification of long-term annual pattern of meteorological drought based on spatiotemporal methods: evaluation of different geostatistical approaches.NATURAL HAZARDS,76(1),515-541.
MLA Bayat, Bardia,et al."Identification of long-term annual pattern of meteorological drought based on spatiotemporal methods: evaluation of different geostatistical approaches".NATURAL HAZARDS 76.1(2015):515-541.
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