Multi-Index Approach to Assess and Monitor Meteorological and Agricultural Drought in the Mediterranean Region: Case of the Upper Oum Er Rabia Watershed, Morocco

被引:1
|
作者
Mliyeh, Mohammed Mouad [1 ]
Brahim, Yassine Ait [2 ]
Koutsovili, Eleni-Ioanna [3 ]
Tzoraki, Ourania [4 ]
Zian, Ahmed [5 ]
Aqnouy, Mourad [6 ]
Benaabidate, Lahcen [1 ]
机构
[1] Sidi Mohammed Ben Abdellah Univ, Fac Sci & Tech, Lab Funct Ecol & Environm Engn, Fes 30000, Morocco
[2] Univ Mohammed VI Polytech, Int Water Res Inst, Benguerir 43150, Morocco
[3] Scuola Super Sant Anna, Inst Crop Sci, I-56010 Pisa, Italy
[4] Univ Aegean, Sch Environm, Dept Marine Sci, Mitilini 81100, Greece
[5] Natl Sch Appl Sci, Lab Engn Sci & Applicat, Al Hoceima 32003, Morocco
[6] Moulay Ismail Univ Meknes, Fac Sci & Tech, Dept Geosci, Appl Geol Res Lab AGRSRT, Errachidia 52000, Morocco
关键词
Mediterranean; multi-index; drought monitoring; climate change; Oum Er Rabia; Morocco; STRESS INDEX CWSI; SOIL-MOISTURE; HYPERSPECTRAL INDEXES; VEGETATION INDEXES; SURFACE; LEAF; PERFORMANCE; MORTALITY; SCALE;
D O I
10.3390/w16213104
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Drought is a severe disaster, increasingly exacerbated by climate change, and poses significant challenges worldwide, particularly in arid and semi-arid regions like Morocco. This study aims to assess and monitor drought using a multi-index approach to provide a comprehensive understanding of its spatio-temporal dynamics at both meteorological and agricultural levels. The research focuses on the Upper Oum Er Rabia watershed, which spans 35,000 km2 and contributes approximately a quarter of Morocco's renewable water resources. We propose a methodology that combines ERA5 temperature data from remote sensing with ground-based precipitation data to analyze drought characteristics. Three meteorological indices were utilized: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Reconnaissance Drought Index (RDI). Additionally, three remote-sensing indices were employed to capture agricultural drought: the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Crop Water Stress Index (CWSI), with a total of 528 NDVI and EVI images and 1016 CWSI images generated through Google Earth Engine (GEE), using machine-learning techniques. Trend analyses were conducted to monitor drought patterns spatio-temporally. Our results reveal that the three-month interval is critical for effective drought monitoring and evaluation. Among the indices, SPEI emerged as the most effective for capturing drought in combination with remote-sensing data, while CWSI exhibited the highest correlation with SPEI over the three-month period, outperforming NDVI and EVI. The trend analysis indicates a significant precipitation deficit, alongside increasing trends in temperature and evapotranspiration over both the short and long term. Furthermore, all drought indices (SPI, SPEI, and RDI) demonstrate an intensification of drought conditions. Adaptation strategies are essential for managing water resources in the Upper Oum Er Rabia watershed under these evolving climate conditions. Continuous monitoring of climate variables and drought indices will be crucial for tracking changes and informing future water management strategies.
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页数:25
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