Spatio-temporal calibration of Hargreaves–Samani model in the Northern Region of Nigeria

Ogunrinde, Akinwale T.; Emmanuel, Israel; Enaboifo, M.A.; Ajayi, Taiwo Adedayo; Pham, Quoc Bao

doi:10.1007/s00704-021-03897-2

Cited by 10 publications

(3 citation statements)

References 42 publications

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“…Focusing on the HS equation (the alternative model of the PM), the calibration has proven its efficiency in various regions across the globe, such as Egypt [15], Bulgaria [3], Sudan and South Sudan [5], West Texas [19], and Nigeria [20]. Additionally, the accuracy of the calculated PET is affected by the configuration of the regional climate model (e.g., the RegCM4) used for this purpose.…”

Section: Discussionmentioning

confidence: 99%

On the Sensitivity of Potential Evapotranspiration in Egypt to Different Dynamical Downscaling Options and Boundary Layer Schemes Using a High-Resolution Regional Climate Model

Anwar,

Srivastava

2023

Asec 2023

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Section: Discussionmentioning

confidence: 99%

On the Sensitivity of Potential Evapotranspiration in Egypt to Different Dynamical Downscaling Options and Boundary Layer Schemes Using a High-Resolution Regional Climate Model

Anwar,

Srivastava

2023

Asec 2023

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“…Prior to obtaining the four MD indices, the program was fed with monthly data of P, Tmax and Tmin, previously calculated through daily data series. Two intermediate calculations were (1) PET, by the Hargreaves method [41] and (2) the aridity index (AI), by the UNEP method [42][43][44]. To obtain aSPI and eRDI, the effective P was calculated using the USDA method (CROPWAT version), as it is a widely used method for arid and semi-arid climates [22,45].…”

Section: Determination Of Each Of the Indices Of Meteorological Droug...mentioning

confidence: 99%

Sensitivity of Four Indices of Meteorological Drought for Rainfed Maize Yield Prediction in the State of Sinaloa, Mexico

et al. 2022

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In the state of Sinaloa, rainfall presents considerable irregularities, and the climate is mainly semiarid, which highlights the importance of studying the sensitivity of various indices of meteorological drought. The goal is to evaluate the sensitivity of four indices of meteorological drought from five weather stations in Sinaloa for the prediction of rainfed maize yield. Using DrinC software and data from the period 1982–2013, the following were calculated: the standardized precipitation index (SPI), agricultural standardized precipitation index (aSPI), reconnaissance drought index (RDI) and effective reconnaissance drought index (eRDI). The observed rainfed maize yield (RMYob) was obtained online, through free access from the database of the Agrifood and Fisheries Information Service of the government of Mexico. Sensitivities between the drought indices and RMYob were estimated using Pearson and Spearman correlations. Predictive models of rainfed maize yield (RMYpr) were calculated using multiple linear and nonlinear regressions. In the models, aSPI and eRDI with reference periods and time steps of one month (January), two months (December–January) and three months (November–January), were the most sensitive. The correlation coefficients between RMYob and RMYpr ranged from 0.423 to 0.706, all being significantly different from zero. This study provides new models for the early calculation of RMYpr. Through appropriate planning of the planting–harvesting cycle of dryland maize, substantial socioeconomic damage can be avoided in one of the most important agricultural regions of Mexico.

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“…Owing to the exclusion of the role of wind speed in the former method, Raziei and Pereira (2013) observed discrepancies between ETo estimates of HAR‐85 and FAO‐PM methods in arid and hyper‐arid climates of eastern and southern Iran. Ogunrinde et al (2022) attributed the better performance after calibration under sub‐humid and humid regions compared with arid and semi‐arid regions of northern Nigeria to the peculiarity of the stations, resolution of the data and climatological features, including high variations in the wind speed and relative humidity. The review and investigation carried out by Shahidian et al (2013) of the most promising parameters used for calibrating the HAR‐85 for California and Bolivia showed some interesting results.…”

Section: Introductionmentioning

confidence: 99%

Correcting Hargreaves‐Samani formula using geographical coordinates and rainfall over different timescales

Elagib

Musa

2023

Hydrological Processes

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The United Nations Food and Agricultural Organization (FAO) approved the Hargreaves-Samani formula (HAR-85) as an alternative to the standard Penman-Monteith method (FAO-PM) for estimating grass reference evapotranspiration (ETo).With much less data demand, HAR-85 is unequivocally useful where meteorological variables are often scarce, incomplete or unavailable. Herein, we evaluate HAR-85 against FAO-PM across 2.505 million km 2 , representing Sudan and South Sudan and encompassing wide hydroclimate domains including the Nile River. We further propose simple year-round and seasonal adjustment models to correcting HAR-85 across the entire study area. The models express HAR-85's error in multiple linear regressions in terms of latitude, longitude, altitude and/or monthly rainfall. Varying data periods, including odd, even and all years, are used in the evaluation and the adjustment models development and validation processes to investigate the influence of changing data period. A suit of eight performance indicators shows dependency of the original bias of HAR-85 on the geographical location, monthly rainfall amount, season of the year and data period. All error indicators amplify southward from the hyper-arid region to the dry sub-humid zone. For example, the mean bias error (MBE) ranges from À0.51 to 1.29 mm/day, respectively. Study area-wide, HAR-85 least represents FAO-PM during the hottest month and the transitional month (between the wet and dry-cool seasons) with MBE of 0.65 and 0.70 mm/day, respectively. Conversely, it represents FAO-PM the most in the wettest month, with smallest MBE of 0.32 mm/day. Beholding this spatiotemporal trait, the final yearly and seasonal adjustment models developed herein enormously moderate the predominant overestimation of the original HAR-85. The former model explains 46.7% of the error variance whereas 36.9% to 62.3% of the variation in the error is explainable by the latter models. These adjustment models narrow the monthly MBE among the stations from À0.71-2.17 to À0.80-1.20 and À0.65-0.99 mm/day, respectively. Without undermining the accuracy, the year-round adjustment model can still be feasibly recommended for general use across the study area.

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Spatio-temporal calibration of Hargreaves–Samani model in the Northern Region of Nigeria

Cited by 10 publications

References 42 publications

On the Sensitivity of Potential Evapotranspiration in Egypt to Different Dynamical Downscaling Options and Boundary Layer Schemes Using a High-Resolution Regional Climate Model

On the Sensitivity of Potential Evapotranspiration in Egypt to Different Dynamical Downscaling Options and Boundary Layer Schemes Using a High-Resolution Regional Climate Model

Sensitivity of Four Indices of Meteorological Drought for Rainfed Maize Yield Prediction in the State of Sinaloa, Mexico

Correcting Hargreaves‐Samani formula using geographical coordinates and rainfall over different timescales

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