Spatial distribution and evaluation of aridity indices in Northern Algeria

Derdous, Oussama; Tachi, Salah Eddine; Bouguerra, Hamza

doi:10.1080/15324982.2020.1796841

Cited by 21 publications

(7 citation statements)

References 20 publications

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“…According to the original definition, the UNEP Aridity Index ( AI UNEP ) is used to analyse the availability or deficit of water on a monthly, seasonal and annual basis, as a ratio of the total amount of precipitation ( Tp ) and potential evapotranspiration ( PET ) (Gao and Giorgi, 2008; Ludwig et al , 2018; Derdous et al , 2021). The AI UNEP is a non‐dimensional quantity, defined as:

{AI}_{UNEP} = \frac{Tp}{PET},

where all values have the previously defined meaning.…”

Section: Study Area and Methodsmentioning

confidence: 99%

Malaco temperature reconstructions and numerical simulation of environmental conditions in the southeastern Carpathian Basin during the Last Glacial Maximum

Ludwig

Gavrilov

Radaković

et al. 2021

J Quaternary Science

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We investigate the glacial climate conditions in the southeastern Carpathian Basin (Vojvodina, Serbia) based on the reconstruction of malacological palaeotemperatures and results from a high‐resolution regional climate simulation. Land snail assemblages from eight loess profiles are used to reconstruct July temperatures during the Last Glacial Maximum (LGM). The malacological reconstructed temperatures are in good agreement with the simulated LGM July temperatures by the Weather Research and Forecast model. Both methods indicate increasing temperatures from the northwestern towards the southeastern parts of the study area. LGM aridity indices calculated based on the regional climate model data suggest more arid conditions in the southeastern parts compared with more humid conditions in the northwestern parts. However, for present‐day conditions, the moisture gradient is reversed, exhibiting more humid (arid) conditions in the southeast (northwest). An explanation for the reversed LGM aridity pattern is provided by an analysis of the prevailing wind directions over the South Banat district (Serbia). The prevailing moist northwesterly winds during summer are not able to compensate for the annual lack of moisture induced by the dry winds from the southeast that are more frequent during the LGM for the other seasons.

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{AI}_{UNEP} = \frac{Tp}{PET},

where all values have the previously defined meaning.…”

Section: Study Area and Methodsmentioning

confidence: 99%

Malaco temperature reconstructions and numerical simulation of environmental conditions in the southeastern Carpathian Basin during the Last Glacial Maximum

Ludwig

Gavrilov

Radaković

et al. 2021

J Quaternary Science

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“…where AI = aridity index; P = precipitation. Researchers in drought monitoring studies have used this AI concept, and it is still used today [23][24][25][26][27][28][29]. In addition, Penman [16] developed a new formula to calculate varied surfaces' evapotranspiration and solely restricted the term to "evaporation".…”

Section: Pet Backgroundmentioning

confidence: 99%

Misconceptions of Reference and Potential Evapotranspiration: A PRISMA-Guided Comprehensive Review

Raza

Al‐Ansari

et al. 2022

Hydrology

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One of the most important parts of the hydrological cycle is evapotranspiration (ET). Accurate estimates of ET in irrigated regions are critical to the planning, control, and regulation of agricultural natural resources. Accurate ET estimation is necessary for agricultural irrigation scheduling. ET is a nonlinear and complex process that cannot be calculated directly. Reference evapotranspiration (RET) and potential evapotranspiration (PET) are two primary forms of ET. The ideas, equations, and application areas for PET and RET are different. These two terms have been confused and used interchangeably by researchers. Therefore, terminology clarification is necessary to ensure their proper use. The research indicates that PET and RET concepts have a long and distinguished history. Thornthwaite devised the original PET idea, and it has been used ever since, although with several improvements. The development of RET, although initially confused with that of PET, was formally defined as a standard method. In this study, the Preferred Reporting Item for Systematic reviews and Meta-Analysis (PRISMA) was used. Equations for RET estimation were retrieved from 44 research articles, and equations for PET estimation were collected from 26 studies. Both the PET and RET equations were divided into three distinct categories: temperature-based, radiation-based, and combination-based. The results show that, among temperature-based equations for PET, Thornthwaite's (1948) equation was mentioned in 12,117 publications, whereas among temperature-based equations for RET, Hargreaves and Samani's (1985) equation was quoted in 3859 studies. Similarly, Priestley (1972) had the most highly cited equation in radiation-based PET equations (about 6379), whereas Ritchie (1972) had the most highly cited RET equations (around 2382) in radiation-based equations. Additionally, among combination-based PET equations, Penman and Monteith's (1948) equations were cited in 9307 research studies, but the equations of Allen et al. (1998) were the subject of a significant number of citations from 23,000 publications. Based on application, PET is most often applied in the fields of hydrology, meteorology, and climatology, whereas RET is more frequently utilized in the fields of agronomy, agriculture, irrigation, and ecology. PET has been used to derive drought indices, whereas RET has been employed for single crop and dual crop coefficient approaches. This work examines and describes the ideas and methodologies, widely used equations, applications, and advanced approaches associated with PET and RET, and discusses future enhancements to increase the accuracy of ET calculation to attain accurate agricultural irrigation scheduling. The use of advanced tools such as remote sensing and satellite technologies, in addition to machine learning algorithms, will help to improve the accuracy of PET and RET estimates. Researchers will be able to distinguish between PET and RET in the future with the use of the study's results.

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“…Under current global climate change trends, extensive semi-arid and desert fringe ecosystems experience declining water resources and increasing atmospheric temperatures (e.g., Tuel and Eltahir, 2020;Pugnaire, et al, 2019;Polade et al, 2017;Maestre et al, 2012;Blondel et al, 2010;Giorgi and Lionello, 2008;Mariotti, 2008;Aussenac, 2002). Since its introduced by De Martonne (1926), the Aridity Index (AI) with its Thornthwaite (Huschke,1959) , Budyko (1974) and UNEP (Barrow, 1992;Greve et al, 2019) versions is widely used to monitor the spread of aridity across these ecosystems (e.g., Pellicone, 2019;Speich, 2019;Traore et al, 2020;Derdous et al, 2021;;and Zomer, 2022). Other aridity and dryness indicators were calculated based on the difference between precipitation and potential evapotranspiration with Bussay et al, (2012) and Vincente-Serrano et al, (2012) relating to it as Water Deficit (WD) while Stephenson (1990), Saha et al, (2020), Kuang-Yu et al (2018), Abatzoglou et al, (2018),…”

Section: Introductionmentioning

confidence: 99%

Climate and aridity measures relationships with spectral vegetation indices across desert fringe shrublands in the South-Eastern Mediterranean Basin

Shoshany

Mozhaeva

2023

Environ Monit Assess

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Mediterranean regions are hot spots of climate change, where the expected decrease in water resources threatens the sustainability of shrublands at their arid margins. Studying spectral vegetation indices relationships with rainfall and Potential Evapotranspiration (PET) changes across Mediterranean to arid transition zones is instrumental for developing methods for mapping and monitoring the effects of climate change on desert fringe shrublands. Here we examined relationships between 17 spectral vegetation indices (VIs) and four climate and aridity measures: rainfall, PET, Aridity Index (AI) and Water Deficit (WD) calculated at accumulation lags between 1 and 6 months. For this purpose, VIs for 38 sites (100x100 meters each) representing less disturbed areas were extracted from Sentinel 2A images for 3 years with high (2016), low (2017), and average (2018) annual rainfall. Most of the VIs had shown the highest correlation with the four climate and aridity measures at 2 months accumulation interval. While NDVI relationships with climate measures gained the widest use, our data suggest that indices combining NIR and SWIR bands better correlate climate parameters. AI is one of the leading annual measures of dryness worldwide, when calculating it monthly, WD was found to better represent the balance between precipitation and PET across the climate transition zone, and to be better correlated with VI's. Relationships between NIR and SWIR VIs and Water Deficit may thus facilitate improvements in monitoring and mapping desert fringe shrublands responses to climate change if supported by similar results from wider areas.

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Spatial distribution and evaluation of aridity indices in Northern Algeria

Cited by 21 publications

References 20 publications

Malaco temperature reconstructions and numerical simulation of environmental conditions in the southeastern Carpathian Basin during the Last Glacial Maximum

Malaco temperature reconstructions and numerical simulation of environmental conditions in the southeastern Carpathian Basin during the Last Glacial Maximum

Misconceptions of Reference and Potential Evapotranspiration: A PRISMA-Guided Comprehensive Review

Climate and aridity measures relationships with spectral vegetation indices across desert fringe shrublands in the South-Eastern Mediterranean Basin

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