Estimation of a trend of meteorological and hydrological drought over Qinhuai River Basin

Abro, Mohammad Ilyas; Elahi, Ehsan; Chand, Ram; Zhu, Dehua; Muhammad, Jan; Daudpoto, Muhammad Rafique; Soomro, Abdul Majid; Khaskheli, Murad Ali

doi:10.1007/s00704-021-03870-z

Cited by 16 publications

(7 citation statements)

References 80 publications

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“…The high correlation of the three-month reference period (November-January) between the SPI, aSPI, RDI and eRDI indices and RMY ob is similar to that reported by [11], who argue that in the presence of MD, using RDI for reference periods of one and two months, the yield of sorghum can be successfully correlated; however, for 3-month periods, there may be a clear improvement in the early identification of drought in most cases [20].…”

Section: Discussionsupporting

confidence: 83%

“…Data from 70 weather stations in the state of Sinaloa were downloaded from the CLImate COMputing (CLICOM) database [36] (http://clicomex.cicese.mx, accessed on 31 December 2021) and analyzed. These weather stations were selected for the following characteristics: (1) daily availability of the data series (P, Tmax and Tmin) >95% [37], (2) data ≥30 years [20] and (3) annual data of RMYob = 100%. The missing P, Tmax and Tmin data were estimated by means of a simple imputation method (nearest neighbor), as [38,39] note that when the percentage of missing data <5, any imputation method works well.…”

Section: Study Areamentioning

confidence: 99%

“…(1) daily availability of the data series (P, Tmax and Tmin) >95% [37], (2) data ≥30 years [20] and (3) annual data of RMY ob = 100%. The missing P, Tmax and Tmin data were estimated by means of a simple imputation method (nearest neighbor), as [38,39] note that when the percentage of missing data < 5, any imputation method works well.…”

Section: Observed Rainfed Corn Yield (Rmyob)mentioning

confidence: 99%

“…To establish suitable models of RMY pr for Sinaloa, it is vital to assess sensitivity by calculating correlations between RMY ob and various MD indices with different reference periods and early time steps [11,19,20].…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionsupporting

confidence: 83%

Section: Study Areamentioning

confidence: 99%

Section: Observed Rainfed Corn Yield (Rmyob)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…The standardized precipitation index (SPI) is one of the most widely used indices in the world due to its user-friendly structure, low need for climatic variables (rainfall only), and ability to determine the severity of drought at different time scales such as 1-, 3-, 6-, 9-, 12-, and 24-month (Sahana et al 2021;Khanmohammadi et al 2022;Noguera et al 2022;Salvacion 2022). Abro et al (2022) evaluated the trend of the SPI in the Qinhuai River Basin using the Mann-Kendall (MK) and Sen's slope (Ss) tests. This research showed that the SPI, in January and September, had significant increasing and decreasing trends, respectively.…”

Section: Introductionmentioning

confidence: 99%

Assessing the performance of SN-SPI and SPI and the trend assessment of drought using the XI correlation technique over Iran

Ghasemi

Mokarram

Zarei

2022

Journal of Water and Climate Change

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In recent decades, human activities such as the excessive production of carbon dioxide gas, deforestation, overexploitation of natural resources, and so on have caused changes in the climate regimes and occurrence frequency of droughts. Thus, the investigation of the trend of changes in drought can be effective in better understanding the drought condition and also more properly managing the adverse effects of drought. Therefore, in this study, the trend of changes in drought was assessed based on the standardized precipitation index (SPI) and the spatially normalized SPI (SN-SPI) in 34 stations over Iran on 1-, 3-, 6-, and 12-month time scales from 1967 to 2019 using the XI correlation technique (XICOR). Then, using the mean residual error (MRE) index and also a comparison of the correlation coefficient (CC) between the SPI and SN-SPI values with a perfect reliable line (line with the equation Y=X- or T-Statistics index), the performances of the SPI and SN-SPI were investigated. The results of the trend assessment indicated that the SPI and SN-SPI had almost the same performance in all chosen time scales (including 1-, 3-, 6-, and 12-month). However, the results of the T- Statistics and MRE revealed that the calculated values of the SN-SPI in more than 80% of the chosen stations (in the selected time scales) were different from the SPI, and in more than 70% of stations, the calculated values of the SN-SPI were less than the SPI. Therefore, it can be said that the SN-SPI has a different performance from the SPI, and under the influence of spatial normalization of the precipitation values in the SN-SPI, the overall level of the calculated SN-SPI values will be decreased.

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Hydrological drought analysis of Yeşilırmak Basin of Turkey by streamflow drought index (SDI) and innovative trend analysis (ITA)

Yüce

DEGER

Eşit

2023

Theor Appl Climatol

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Estimation of a trend of meteorological and hydrological drought over Qinhuai River Basin

Cited by 16 publications

References 80 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

Assessing the performance of SN-SPI and SPI and the trend assessment of drought using the XI correlation technique over Iran

Hydrological drought analysis of Yeşilırmak Basin of Turkey by streamflow drought index (SDI) and innovative trend analysis (ITA)

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