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翁布里亚地区(意大利中部)干旱现象研究中标准化降水蒸散指数(SPEI)与标准化降水指数(SPI)的相关性

SPEI and SPI correlation in the study of drought phenomena in Umbria region (central Italy).

作者信息

Venturi Sara, Dunea Daniel, Mateescu Elena, Virsta Ana, Petrescu Nicolae, Casadei Stefano

机构信息

Department of Civil and Environmental Engineering, University of Perugia, Via Duranti 93, 06125, Perugia, Italy.

Department of Environmental Engineering, Valahia University of Targoviste, Aleea Sinaia No.13, 130004, Targoviste, Romania.

出版信息

Environ Sci Pollut Res Int. 2025 Jan;32(1):168-188. doi: 10.1007/s11356-024-35740-2. Epub 2024 Dec 15.

DOI:10.1007/s11356-024-35740-2
PMID:39674986
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11717805/
Abstract

This study aims to analyze the performances and correlation of the standardized precipitation index (SPI) and standardized precipitation evaporation index (SPEI) from the perspective of supplying effective indicators for drought risk management prevention. Indices have been evaluated using long time series of precipitation and temperature data (from 1961 to 2020) gauged and validated in the land monitoring system of the Umbria region (central Italy). Results show how SPEI can evaluate better the drought phenomena, both in terms of occurred events and in terms of trends. In particular, SPEI can appreciate the effects of the increase in temperatures, which in the next years could be predominant in c limate change. Currently, the high correlation between SPEI and SPI (R > 0.8 and r > 0.9) makes possible the use of SPI instead of SPEI in drought analysis; however, this correlation, evaluated on the two times series 1961-1990 and 1991-2020, shows a decreasing tendency; then, it could be no longer reliable in the future. These results should lead to increasingly synergistic monitoring of rainfall and temperature data, limiting as much as possible the lack of temporal overlap of the two sensors at the gauging stations. The possibility of using ERA 5 reanalysis data was also explored for the years that presented severe droughts by comparing them to the station-based observations. While the temperature data is reliable, the total precipitation parameter seems less affordable, and then, other available gridded datasets, e.g., CHIRPS, MERRA2, and Terraclimate, should have to be considered to improve the modeled precipitation's suitability.

摘要

本研究旨在从为干旱风险管理预防提供有效指标的角度,分析标准化降水指数(SPI)和标准化降水蒸发指数(SPEI)的性能及相关性。利用在意大利中部翁布里亚地区的土地监测系统中测量和验证的长时间序列降水和温度数据(1961年至2020年)对指数进行了评估。结果表明,无论是在已发生的事件方面还是在趋势方面,SPEI都能更好地评估干旱现象。特别是,SPEI能够认识到气温上升的影响,在未来几年,气温上升在气候变化中可能占主导地位。目前,SPEI与SPI之间的高相关性(R>0.8且r>0.9)使得在干旱分析中可以使用SPI代替SPEI;然而,在1961 - 1990年和1991 - 2020年这两个时间序列上评估的这种相关性呈下降趋势;那么,未来它可能不再可靠。这些结果应促使对降雨和温度数据进行越来越协同的监测,尽可能减少测量站两个传感器时间重叠不足的情况。还通过将呈现严重干旱年份的ERA 5再分析数据与基于站点的观测数据进行比较,探讨了使用该数据的可能性。虽然温度数据可靠,但总降水参数似乎不太适用,因此,应考虑其他可用的网格化数据集,如CHIRPS、MERRA2和Terraclimate,以提高模拟降水的适用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cae2/11717805/3920800ed5de/11356_2024_35740_Fig13_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cae2/11717805/1da94183e0cc/11356_2024_35740_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cae2/11717805/92adbd4fe5b4/11356_2024_35740_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cae2/11717805/3ec3d0fc45fa/11356_2024_35740_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cae2/11717805/2041ecd92550/11356_2024_35740_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cae2/11717805/839d1e978fc7/11356_2024_35740_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cae2/11717805/fca36c4182d3/11356_2024_35740_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cae2/11717805/d66b75fa86b0/11356_2024_35740_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cae2/11717805/be809e83d356/11356_2024_35740_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cae2/11717805/3f13689473e4/11356_2024_35740_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cae2/11717805/571c7ccaff8e/11356_2024_35740_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cae2/11717805/1f0350a13f32/11356_2024_35740_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cae2/11717805/3920800ed5de/11356_2024_35740_Fig13_HTML.jpg

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本文引用的文献

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Spatiotemporal drought analysis in Bangladesh using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI).利用标准化降水指数 (SPI) 和标准化降水蒸散指数 (SPEI) 分析孟加拉国的时空干旱。
Sci Rep. 2022 Nov 30;12(1):20694. doi: 10.1038/s41598-022-24146-0.
2
Variability and time series trend analysis of rainfall in the mid-hill sub humid zone: a case study of Nauni.中低山区亚湿润区降雨的变异性和时间序列趋势分析:以瑙尼为例。
Environ Sci Pollut Res Int. 2022 Nov;29(53):80466-80476. doi: 10.1007/s11356-022-21507-0. Epub 2022 Jun 18.
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Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey.
比较混合机器学习方法在预测土耳其 Sakarya 气象站短期气象干旱中的应用。
Environ Sci Pollut Res Int. 2022 Oct;29(50):75487-75511. doi: 10.1007/s11356-022-21083-3. Epub 2022 Jun 3.
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Application of smart irrigation systems for water conservation in Italian farms.智能灌溉系统在意大利农场的节水应用。
Environ Sci Pollut Res Int. 2021 Jun;28(21):26488-26499. doi: 10.1007/s11356-021-12524-6. Epub 2021 Jan 23.