Spatiotemporal Drought Characterization Using Gravity Recovery and Climate Experiment (GRACE) in the Central Plateau Catchment of Iran

Nemati, Ahmad; Joodaki, Gholamreza; Nadoushani, S. Saeid Mousavi

doi:10.1007/s40710-019-00413-7

Cited by 16 publications

(4 citation statements)

References 60 publications

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“…The equations of models 15-17 were sensitive, which agrees with [11,22,46,54], who argue that in arid and semi-arid climates, the MD indices are more sensitive when effective P is used early.…”

Section: Discussionsupporting

confidence: 77%

“…According to Table 3, the highest r'P and r'S, which were registered for aSPI-3 (November-January) and eRDI-3 (November-January), can be attributed to the fact that effective P, implicit in the calculation of these two indexes, is more sensitive to variations in the use of rainfed maize water [46,54], in addition to the fact that aSPI is an index that should be used preferentially in semi-arid climates, especially when increased sensitivity is desired [11,22,46].…”

Section: Discussionmentioning

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: 77%

Section: Discussionmentioning

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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“…Iran is divided into six main catchments [19], and the Central Plateau, with an area of approximately 825,000 km 2 , consists of 51% of the total area of Iran. According to the precipitation, this basin can be divided into three parts: semiarid, arid, and very arid, with an average annual rainfall of 330, 125, and 75 mm [19,20]. The average long-term rainfall in the Central Plateau is approximately 155 mm/yr, while the minimum value was recorded in 2008 with 45 mm/yr of precipitation.…”

Section: Study Areamentioning

confidence: 99%

“…In addition, the MLC method is a simple pixel-based method that is often used to detect land-use changes [18]. However, MLC is a good method for classification; however, sometimes, it is not able to classify mixed pixels correctly, which may lead to the loss of some information [19].…”

Section: Maximum Likelihood Classificationmentioning

confidence: 99%

Satellite-Based Monitoring of Growing Agricultural Water Consumption in Hyper-Arid Regions

Ebrahimivand,

Hooshyaripor,

Rezaei-Gharehaghaj

et al. 2023

Water

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Land-use change has a key role in hydrologic processes and biodiversity. Although many satellite-based studies have been conducted to reveal the interaction between land-use changes in hydrological processes worldwide, the land-use change impact on agricultural water consumption in hyper-arid regions is poorly understood. Here, we investigate increased agricultural water consumption in the Qom province, a hyper-arid region in Iran, using derived imageries from Landsat 5 Tm and Landsat 8 OLI during the last three decades. We used maximum likelihood classification (MLC) and decision tree classification (DTC) to analyze the satellite images. The MLC method showed that croplands have increased from 30,547 ha in 1989 to 39,255 ha in 2019 (i.e., a 29% increase). In this period, the total orchard area increased from 3904 ha to 6307 ha, revealing a growth of 61%. In the DTC method, the increases in the cropland and orchard areas were, respectively, 34% and 60%. Although both MLC and DTC satisfied the accuracy criteria, the former was more consistent than the latter concerning ground data and documented statistics. Satellite-based and MLC results showed an increase in agricultural water consumption from 152 million cubic meters (MCM) in 1989 to 209 MCM in 2019, showing a 38% increase (i.e., 1.27% annually). Our findings send an alarming message for policymakers to reduce the water demand through sustainable agricultural practices in the Qom province, where the agricultural sector uses approximately 90% of annual water demand.

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Using multivariate adaptive regression splines and extremely randomized trees algorithms to predict dust events frequency around an international wetland and prioritize its drivers

Ebrahimi-Khusfi

Nafarzadegan

Khosroshahi

2021

Environ Monit Assess

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Spatiotemporal Drought Characterization Using Gravity Recovery and Climate Experiment (GRACE) in the Central Plateau Catchment of Iran

Cited by 16 publications

References 60 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

Satellite-Based Monitoring of Growing Agricultural Water Consumption in Hyper-Arid Regions

Using multivariate adaptive regression splines and extremely randomized trees algorithms to predict dust events frequency around an international wetland and prioritize its drivers

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