Spatiotemporal analysis of aridity indices by using the nonparametric methods (case study: Sirvan river basin, Kurdistan Province, Iran)

Bahmani, Shadi; Salimi, Hana; Hadi, Syamsul

doi:10.1007/s12517-021-07813-w

Cited by 3 publications

(1 citation statement)

References 24 publications

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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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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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Spatiotemporal changes in global aridity in terms of multiple aridity indices: An assessment based on the CRU data

Ullah

You

Sachindra

et al. 2022

Atmospheric Research

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Spatial-temporal variability of climate indices in Chungcheong provinces of Korea: Application of graphical innovative methods for trend analysis

Adelodun

Odey²,

Cho³

et al. 2022

Atmospheric Research

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Spatiotemporal analysis of aridity indices by using the nonparametric methods (case study: Sirvan river basin, Kurdistan Province, Iran)

Cited by 3 publications

References 24 publications

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

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

Spatiotemporal changes in global aridity in terms of multiple aridity indices: An assessment based on the CRU data

Spatial-temporal variability of climate indices in Chungcheong provinces of Korea: Application of graphical innovative methods for trend analysis

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