Drought Analysis Using Standardized Evapotranspiration and Aridity Index at Bilate Watershed: Sub-Basins of Ethiopian Rift Valley

Haile, Bereket Tesfaye; Bekitie, Kassahun Ture; Zeleke, Tadesse Terefe; Ayal, Desalegn Yayeh; Feyisa, Gudina Legese; Anose, Fikiru Abiko

doi:10.1155/2022/1181198

Cited by 12 publications

(6 citation statements)

References 54 publications

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“…Drought was so frequent that at least one or more drought events were observed annually at all stations. Drought frequency has increased from time to time, as investigated by Haile et al (2022) in the Bilate watershed and the Rift Valley Lakes Basin by Tesfamariam et al (2019) where their findings agree with the results of this paper. Yirga (2021) also observed frequent and extreme drought conditions in the eastern parts of the Guraghe zone in the central part of Ethiopia between 1989 and 2017.…”

Section: Discussionsupporting

confidence: 89%

Spatiotemporal analysis of drought severity using SPI and SPEI: case study of semi-arid Borana area, southern Ethiopia

Worku

2024

Front. Environ. Sci.

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Drought is a complex natural hazard that can cause damage to socio-economic and biophysical systems in any climatic region. The main objective of this paper was to analyze the spatiotemporal drought in the semi-arid Borana area of southern Ethiopia from 1981 to 2018. The climate data was obtained from the Ethiopian Meteorological Institute. Standardized precipitation index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) at 3- and 12-month timescales were adopted to characterize drought during these periods and wet seasons in time and space. The results revealed that moderate to extreme drought conditions at various stations frequently hit months stretching from March to June and September to November. The years 1984, 1985, 1992, 1999, 2000, 2001 and 2011 were known to be the driest years, among others. A total of 484, 563, 573 and 620 combined moderate, severe and extreme drought events were observed across all stations using SPI-3, SPEI-3, SPI-12 and SPEI-12, respectively. Internal variability among the stations was noticed regarding the frequency and magnitude of drought. Ganna (March to May), the season that was supposed to be the main rainy season in the region, was by far drier than Hagayya (September to November), the short rainy period. In general, this study found a high frequency and intense magnitude of drought that varies across the stations. Therefore, any localized adaptation and mitigation strategies intended to tackle or minimize drought-associated impacts should take into account this local variability.

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Section: Discussionsupporting

confidence: 89%

Spatiotemporal analysis of drought severity using SPI and SPEI: case study of semi-arid Borana area, southern Ethiopia

Worku

2024

Front. Environ. Sci.

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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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“…For this reason, the workload, device budget, calibration costs arising from measurement continuity, and climatic conditions restrict the station data. Thus, several methods and empirical equations have been investigated to obtain EP values due to the data limitations and/or ungauged places [13][14][15][16][17][18][19]. For instance, Haile et al [13] used the Hargreaves empirical method, which required precipitation and the minimum and maximum temperature to estimate the evaporation in their study.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, several methods and empirical equations have been investigated to obtain EP values due to the data limitations and/or ungauged places [13][14][15][16][17][18][19]. For instance, Haile et al [13] used the Hargreaves empirical method, which required precipitation and the minimum and maximum temperature to estimate the evaporation in their study. The Standardized Evapotranspiration Indexes were applied in the study to examine the magnitudes of the spatiotemporal patterns of drought at the Bilate Basin in Ethiopia.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Completing Missing Data Series in Pan Evaporation Using Multi-Meteorologic Phenomena

Dis

2023

Sustainability

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The most crucial losses in the hydrological cycle occur due to evaporation (EP). As a result, the accurate attainment of this complex phenomenon is critical in studies on irrigation, efficiency in the basins, dams, continuous hydrometeorological simulations, flood frequency, and water budget analysis. However, EP data sets are expensive, difficult to sustainably measure, and scarce, also, predictions are challenging tasks due to the wide range of parameters involved in these processes. In this study, the data gaps are filled with Class A evaporation pan observations through building a new meteorological station during seasons with no gauge measurements available for a three-year time period. These observations demonstrate high correlations with the readings from the Meteorology Airport Station, with a PCC of 0.75. After the continuous EP time series was completed over Kahramanmaras, these values were retrieved non-linearly via an artificial intelligence model using multi-meteorological parameters. In the study, the simulation performance is evaluated with the help of eight different statistical metrics in addition to graphical representations. The evaluation reveals that, when compared to the other EP functions, using both temperature and wind-driven simulations has the highest correlation (PCC = 0.94) and NSCE (0.87), as well as the lowest bias (PBias = −1.65%, MAE = 1.27 mm d−1, RMSD = 1.6 mm d−1, CRMSE = 24%) relative to the gauge measurements, while they give the opposite results in the solely precipitation-based models (PCC = 0.42, NSCE = 0.17, PBias = −6.44%, MAE = 3.58 mm d−1, RMSD = 4.2 mm d−1, CRMSE = 62%). It has been clearly seen that the temperature parameter is the most essential factor, while precipitation alone may be insufficient in EP predictions; additionally, wind speed and relative humidity would improve the prediction performance in artificial intelligence techniques.

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Drought Analysis Using Standardized Evapotranspiration and Aridity Index at Bilate Watershed: Sub-Basins of Ethiopian Rift Valley

Cited by 12 publications

References 54 publications

Spatiotemporal analysis of drought severity using SPI and SPEI: case study of semi-arid Borana area, southern Ethiopia

Spatiotemporal analysis of drought severity using SPI and SPEI: case study of semi-arid Borana area, southern Ethiopia

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

A New Approach for Completing Missing Data Series in Pan Evaporation Using Multi-Meteorologic Phenomena

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