Predictive association between meteorological drought and climate indices in the state of Sinaloa, northwestern Mexico

Cárdenas, Omar Llanes

doi:10.1007/s12517-022-11146-7

Cited by 3 publications

(4 citation statements)

References 46 publications

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“…Furthermore, the results of this study are similar to those reported by [75], since these authors point out that, IBY-RBY in Sinaloa and for the autumn-winter cycle of 2021-2022, was 1.93 t ha -1 , being fourth place nationally in sown area (85,657 hectares). The results of ASM can be attributed to the fact that, in 1986 (July-September), minimal anomalies occurred: negative ones from the Atlantic multidecadal oscillation and positive ones from the Pacific decadal oscillation, which were generators of La Niña events [76]. Furthermore, according to data from [77] cited by [74], points out that the period 2003-2005 was the one that presented the greatest extreme wet events in northwest Mexico.…”

Section: Discussionmentioning

confidence: 98%

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Modeling of Yield of Irrigated and Rainfed Bean, Based on Essential Climatic Variables, in the Center and South from Sinaloa State, Mexico

Llanes Cárdenas,

Estrella Gastélum,

Parra Galaviz

et al. 2024

Preprint

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The goal was to model the irrigated (IBY) and rainfed (RBY) bean yields, as a function of essential climatic variables (ECVs), in the center (Culiacán) and south (Rosario) from Sinaloa. In Sinaloa, and for the period 1982–2013 (October–March), the following were calculated: a) temperatures. b) average degree days for the bean, c) cumulative potential evapotranspiration and d) cumulative effective precipitation. For ECVs, from the European Space Agency, e) daily soil moisture. f) IBY and RBY, from the Agrifood and Fisheries Information Service. Multiple linear regressions were applied, which modeled IBY–RBY (dependent variables), as a function of ECVs (independent variables). Then, to establish each Pearson correlation (PC) as significantly different from zero, a hypothesis test was applied: PC vs Pearson's critical correlation (CPC). The four models obtained were significantly predictive: IBY–Culiacán (PC = 0.590 > CPC = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|) and RBY–Rosario (PC = 0.532 > CPC = |0.349|). This study is the first in Sinaloa to predict IBY and RBY based on ECVs, contributing to the production of sustainable food.

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Section: Discussionmentioning

confidence: 98%

“…According to [70], all four models meet the assumption of homogeneity (average residuals = 0). According to [7,76,84], the four models presented normality in the residuals, because no pvalues < 0.05 (Table 4).…”

Section: Discussionmentioning

confidence: 99%

Modeling of Yield of Irrigated and Rainfed Bean, Based on Essential Climatic Variables, in the Center and South from Sinaloa State, Mexico

Llanes Cárdenas,

Estrella Gastélum,

Parra Galaviz

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, the results of this study are similar to those reported by [68], since, as those authors pointed out, IBY-RBY in Sinaloa for the autumn-winter cycle of 2021-2022 was 1.93 t ha −1 , being fourth place nationally in sown area (85,657 hectares). The results of ASM can be attributed to, for example, the fact that in 1986 (July-September), minimal anomalies occurred: negative anomalies from the Atlantic multidecadal oscillation and positive anomalies from the Pacific decadal oscillation, which were generators of La Niña events [69]. Also, the period 1982-1984 recorded ASM < 0.05 m 3 m −3 , which agrees with [70], who for same period found anomalies with +phase Atlantic multidecadal oscillation and -Pacific decadal oscillation, which are generators of La Niña events (absence of humidity).…”

Section: Discussionmentioning

confidence: 99%

“…According to [7,69,78], the four models presented normality in the residuals, because were no p-values < 0.05 (Table 3).…”

Section: Discussionmentioning

confidence: 99%

Modeling Yield of Irrigated and Rainfed Bean in Central and Southern Sinaloa State, Mexico, Based on Essential Climate Variables

Llanes Cárdenas,

Estrella Gastélum,

Parra Galaviz

et al. 2024

Atmosphere

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The goal was to model irrigated (IBY) and rainfed (RBY) bean yields in central (Culiacán) and southern (Rosario) Sinaloa state as a function of the essential climate variables soil moisture, temperature, reference evapotranspiration, and precipitation. For Sinaloa, for the period 1982–2013 (October–March), the following were calculated: (a) temperatures, (b) average degree days for the bean, (c) cumulative reference evapotranspiration, and (d) cumulative effective precipitation. For essential climate variables, (e) daily soil moisture obtained from the European Space Agency and (f) IBY and RBY from the Agrifood and Fisheries Information Service were used. Multiple linear regressions were significant for predicting IBY–RBY (dependent variables) as a function of essential climate variables (independent variables). The four models obtained were significantly predictive: IBY–Culiacán (Pearson correlation (PC) = 0.590 > Pearson critical correlation (CPC) = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|), and RBY–Rosario (PC = 0.532 > CPC = |0.349|). Due to the lack of irrigation depth data, many studies only focus on modeling RBY; this study is the first in Sinaloa to predict IBY and RBY based on essential climate variables, contributing to the production of sustainable food.

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Calculation of indicators of maximum extreme temperature in Sinaloa state, northwestern Mexico

Llanes Cárdenas,

Cervantes Arce,

González González

2023

Earth sci. res. j.

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One of the climate problems that causes the most environmental impact worldwide is the trend of increasing occurrence of events of maximum extreme temperature, signaled by indicators such as hot extremes (HE) and maximum maximorum (highest maximum) temperature (MmT). These events can cause conditions ranging from severe droughts to heat stroke, which can cause death in any population. Indicators of maximum extreme temperature in one of the most important agricultural areas in northwestern Mexico were calculated based on significant trends (ST) and adjusted return periods. To calculate the trends of the maximum extreme temperature, frequency (FR), annual average duration (AAD), annual daily duration (ADD), intensity (IN) of HE, and MmT, the Mann-Kendall and Sen’s slope tests were applied to data obtained for 19 weather stations from the CLImate COMputing database for the period 1982–2014. Adjusted return periods (ARP) were calculated for each indicator of maximum extreme temperature by fitting a probability distribution function. For the study area, the ST and maximum extreme temperature shows a prevailing cooling trend. This can be deduced by observing the proportion of negative ST compared with positive ST. The highest positive magnitudes of ST were recorded at stations CUL (FR = 3.44 HE dec-1), GUT (AAD = 6.15 day HE-1 dec-1 and IN = 13.62 °C dec-1), IXP (ADD = 35.00 day dec-1) and POT (MmT = 2.50 °C day-1 dec-1). For ARP, the estimate of the average occurrence frequency of extreme events per100 years are FR = 6.11 HE dec-1 (1 time), AAD = 6.64 day HE-1 dec-1 (4 times), ADD = 38.68 day dec-1 (1 time), IN = 39.09 °C dec-1 (6 times) and MmT = 41.95 °C day-1 dec-1 (1 time). These findings are of key importance for the economic sectors related to agricultural production in the state known, at least to date, as “the breadbasket of Mexico” (Sinaloa). The results will help to develop adaptation/prevention measures before the coming socioeconomic and hydrological disasters.

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Predictive association between meteorological drought and climate indices in the state of Sinaloa, northwestern Mexico

Cited by 3 publications

References 46 publications

Modeling of Yield of Irrigated and Rainfed Bean, Based on Essential Climatic Variables, in the Center and South from Sinaloa State, Mexico

Modeling of Yield of Irrigated and Rainfed Bean, Based on Essential Climatic Variables, in the Center and South from Sinaloa State, Mexico

Modeling Yield of Irrigated and Rainfed Bean in Central and Southern Sinaloa State, Mexico, Based on Essential Climate Variables

Calculation of indicators of maximum extreme temperature in Sinaloa state, northwestern Mexico

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