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Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices

  • Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing the variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of ElTemporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing the variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then, the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Additionally, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100%. However, the seasonal precipitation may increase more than 100% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation.show moreshow less

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Metadaten
Document Type:Article
Author: Majid Dehghani, Somayeh SalehiORCiD, Dr Mosavi, Narjes NabipourORCiD, Shahaboddin ShamshirbandORCiD, Pedram GhamisiORCiD
DOI (Cite-Link):https://doi.org/10.3390/ijgi9020073Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200128-40740Cite-Link
URL:https://www.mdpi.com/2220-9964/9/2/73
Parent Title (German):ISPRS, International Journal of Geo-Information
Publisher:MDPI
Language:English
Date of Publication (online):2020/01/24
Date of first Publication:2020/01/24
Release Date:2020/01/28
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:Volume 9, Issue 2, 73
Pagenumber:23
Tag:Machine learning; seasonal precipitation; spatial analysis; spatiotemporal database; spearman correlation coefficient
GND Keyword:Maschinelles Lernen
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke
BKL-Classification:31 Mathematik
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)