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

The goal is to calculate predictive models capable of making reliable associations between meteorological drought indices (MDr) (standardized precipitation index (SPI), agricultural standardized precipitation index (aSPI), reconnaissance drought index (RDI) and effective reconnaissance drought index...

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Veröffentlicht in:Arabian journal of geosciences 2023, Vol.16 (1), Article 79
1. Verfasser: Cárdenas, Omar Llanes
Format: Artikel
Sprache:eng
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Zusammenfassung:The goal is to calculate predictive models capable of making reliable associations between meteorological drought indices (MDr) (standardized precipitation index (SPI), agricultural standardized precipitation index (aSPI), reconnaissance drought index (RDI) and effective reconnaissance drought index (eRDI)), and climate indices (CI) (Atlantic multidecadal oscillation (AMO), North Atlantic oscillation (NAO), oceanic El Niño index (ONI), and Pacific decadal oscillation (PDO)) from 7 weather stations in Sinaloa for the period 1969–2018. From the National Water Commission (CONAGUA) and the National Meteorological Service (SMN), free online data on precipitation and temperature (maximum and minimum) were obtained. For the calculation of MDr, Drought Indices Calculator (DrinC) software was used. CI were obtained from the National Oceanic and Atmospheric Administration (NOAA 2022 ) online database. To evaluate association, Pearson and Spearman correlations (initial correlations) were applied. For the models, linear and nonlinear regressions were used. To establish whether the correlations (initial and model correlations) were significantly different from 0, a hypothesis test was applied (between the correlation coefficients and the critical correlation coefficients). The CI with the greatest association with MDr are ONI and PDO. Only two stations (La Concha and Rosario) registered significant predictive capacity, expressed in 12 models. At La Concha and Rosario stations, the best indices, scales, and time steps to predict MDr are RDI–3 (Jul–Sept) and aSPI–3 (Jul–Sept), respectively. Although the models had R 2 values of 0.231 ≤ R 2  ≤ 0.384, all the correlations (0.481 ≤ correlations ≤ 0.620) are significantly different from 0. This study provides, for the first time for Sinaloa, models that accurately predict MDr through four CI. Application of these models can prevent overexploitation and contamination of water resources in this purely agricultural state, considered the breadbasket of Mexico.
ISSN:1866-7511
1866-7538
DOI:10.1007/s12517-022-11146-7