About the Journal
In Memory of Professor Jerzy Radecki
Editorial Office
Editorial Board
Scope
Impact Factor
Indexed in...
Journal Impact Factor contributing items
Contact
Subscription
Current issue
Online first
Notes to Authors
Frequently Asked Questions
Editorial policies
PJOES Publication Policy Overview
PJOES Peer Review Policy
PJOES Research Ethics and Malpractice Policy
PJOES Plagiarism Policy
PJOES Conflict of Interest Policy
PJOES Open Access Policy
PJOES Instructions for Reviewers
Generative AI and AI-Assisted Technologies Policy
Archive
ORIGINAL RESEARCH
Agroclimatic Modeling of the Drought
Trend in the State of Sinaloa, Mexico,
Using the PDO–AMO Indices
1
Instituto Politécnico Nacional–Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional
(CIIDIR-IPN–SINALOA)
2
Instituto Politécnico Nacional–Centro Interdisciplinario de Investigaciones y Estudios sobre Medio Ambiente
y Desarrollo (CIIEMAD-IPN)
3
Universidad Autónoma de Sinaloa–Facultad de Ingeniería Mochis (UAS–FIM)
Submission date: 2024-01-15
Final revision date: 2024-03-21
Acceptance date: 2024-04-14
Online publication date: 2024-09-18
Publication date: 2025-01-09
Corresponding author
Pol. J. Environ. Stud. 2025;34(2):1515-1527
KEYWORDS
meteorological drought trendlinear regressionfirst principal componentpredictive modelhypothesis test
TOPICS
ABSTRACT
The goal was to model the trend of meteorological droughts (MeD) using the Pacific decadal
oscillation (PDO) and Atlantic multidecadal oscillation (AMO) indices. PDO–AMO series were obtained
from the National Oceanic and Atmospheric Administration. In 12 weather stations in the state of Sinaloa
(1981–2017), the agricultural standardized precipitation index (aSPI) and reconnaissance drought index
(RDI) were calculated. The linear (SLT) and non-parametric (SNT) significant trends of the aSPI and RDI
were calculated. A principal component analysis was applied to SLT–SNT and the first observed principal
component (Z PC–1o) was extracted. The first calculated principal component was modeled through a
linear regression of Z PC–1c (dependent variable) on PDO–AMO (independent variables). The correlations
between Z PC–1o vs Z PC–1c = 0.522 and the linear trend of Z PC–1c = 0.501, were significantly different
from zero. This study contributes to addressing a research gap not otherwise explored to date in Sinaloa:
modeling of the trend in MeD through aSPI–RDI and PDO–AMO. The model can be used to help schedule
agricultural irrigation at the most productive times.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
REFERENCES (72)
1.
SERRANO V., DOMÍNGUEZ C.S.M., MURPHY F., HANNAFORD C., REIG J., PEÑA A.F., TRAMBLAY D., TRIGO Y., MAC DONALD R.M., LUNA N. Long-term variability and trends in meteorological droughts in Western Europe (1851-2018). *International Journal of Climatology*, 41, E690, 2021. <https://doi.org/10.1002/joc.67...>.
2.
ALAM J., SAHA P., MITRA R., DAS J. Investigation of spatio‑temporal variability of meteorological drought in the Luni River Basin, Rajasthan, India. *Arabian Journal of Geosciences*, 16 (201), 1, 2023. <https://doi.org/10.1007/s12517...>.
3.
GAN R., LI D., CHEN C., YANG F., ZHANG X., GUO X. Spatiotemporal characteristics of extreme hydrometeorological events and potential influencing factors in the Huaihe River Basin, China. *Stochastic Environmental Research and Risk Assessment*, 37, 2693, 2023. <https://doi.org/10.1007/s00477...>.
4.
CLARKE R.T. Fitting and testing the significance of linear trends in Gumbel distributed data. *Hydrology and Earth System Sciences*, 6 (1), 17, 2002. <https://doi.org/10.5194/hess-6...>.
5.
SPINONI J., NAUMANN G., VOGT J., BARBOSA P. European drought climatologies and trends based on a multi-indicator approach. *Global and Planetary Change*, 127, 50, 2015. <https://doi.org/10.1016/j.glop...>.
6.
SYED F.S., ADNAN S., ZAMREEQ A., GHULAM A. Identification of droughts over Saudi Arabia and global teleconnections. *Natural Hazards*, 112, 2717, 2022. <https://doi.org/10.1007/s11069...>.
7.
MANN H.B. Nonparametric tests against trend. *Econometrica*, 13 (3), 245, 1945. <https://doi.org/10.2307/190718...> PMCid:PMC8354445.
9.
ABERA T.K., GEBEYEHU A.A. Hydrological and Meteorological Drought Monitoring and Trend Analysis in Abbay River Basin, Ethiopia. *Advances in Meteorology*, 2022, 2048077, 2022. <https://doi.org/10.1155/2022/2...>.
10.
MERABTI A., DAROUICH H., PAREDES P., MEDDI M., PEREIRA L.S. Assessing spatial variability and trends of droughts in Eastern Algeria using SPI, RDI, PDSI, and MedPDSI. *Water*, 15 (4), 626, 2023. <https://doi.org/10.3390/w15040...>.
11.
LIU W., MA S., FENG K., GONG Y., LIANG L., TSUBO M. Suitability assessment of agricultural drought monitoring indices: A case study in an inland river basin. *Agronomy*, 13 (2), 469, 2023. <https://doi.org/10.3390/agrono...>.
12.
SEN P.K. Estimates of the regression coefficient based on Kendall's Tau. *Journal of the American Statistical Association*, 63 (324), 1379, 1968. <https://doi.org/10.1080/016214...> PMCid:PMC5266778.
13.
GUO W., HUANG S., ZHAO Y., LENG G., ZHAO X., LI P., NIE M., HUANG Q. Quantifying the effects of nonlinear trends of meteorological factors on drought dynamics. *Natural Hazards*, 117, 2505, 2023. <https://doi.org/10.1007/s11069...>.
14.
LLANES C.O., PEINADO G.H.J., MONTIEL M.J., NORZAGARAY C.M., GONZÁLEZ O.H.A., CAMPISTA L.S. Seasonal trend indicators and return periods of meteorological drought in northern Mexico. *Polish Journal of Environmental Studies*, 26 (4), 1471, 2017. <https://doi.org/10.15244/pjoes...>.
15.
TIGKAS D., VANGELIS H., TSAKIRIS G. Drought characterization based on an agriculture‑oriented standardized precipitation index. *Theoretical and Applied Climatology*, 135, 1435, 2019. <https://doi.org/10.1007/s00704...>.
16.
LLANES C.O., NORZAGARAY C.M., GAXIOLA A., PÉREZ G.E., MONTIEL M.J., TROYO D.E. Sensitivity of four meteorological drought indices for rainfed maize yield prediction in Sinaloa, Mexico. *Agriculture*, 12 (4), 525, 2022. <https://doi.org/10.3390/agricu...>.
17.
LLANES C.O. Predictive association between meteorological drought and climate indices in Sinaloa, northwestern Mexico. *Arabian Journal of Geosciences*, 16 (79), 1, 2023. <https://doi.org/10.1007/s12517...>.
18.
TSAKIRIS G. Meteorological Drought Assessment. Prepared for MEDROPLAN—Mediterranean Drought Preparedness and Mitigation Planning, Zaragoza, Spain, 2004.
19.
XUN T.Y., LIN N.J., FENG H.Y. A review on drought index forecasting and their modelling approaches. *Archives of Computational Methods in Engineering*, 30, 1111, 2023. <https://doi.org/10.1007/s11831...>.
20.
ZHANG L., WU M., ZHENG J., HAO Z. Role of solar activity and Pacific decadal oscillation in hydroclimatic patterns of eastern China over the past millennium. *Global and Planetary Change*, 216, 103905, 2022a. <https://doi.org/10.1016/j.glop...>.
21.
LI L., ZHAO L., LI Y. Spatiotemporal characteristics of meteorological and agricultural droughts in China: Change patterns and causes. *Agriculture*, 13 (2), 265, 2023. <https://doi.org/10.3390/agricu...>.
22.
MÉNDEZ J., MAGAÑA R. Regional aspects of prolonged meteorological droughts over Mexico. *Journal of Climate*, 23, 1175, 2010. <https://doi.org/10.1175/2009JC...>.
23.
LLANES C.O., GAXIOLA H.A., ESTRELLA G.R.D., NORZAGARAY C.M., TROYO D.E., PÉREZ G.E., RUIZ G.R., PELLEGRINI C.M. Variability and factors of influence of extreme wet and dry events in northern Mexico. *Atmosphere*, 9 (4), 122, 2018. <https://doi.org/10.3390/atmos9...>.
24.
COMISIÓN NACIONAL DEL AGUA (CONAGUA) – SERVICIO METEOROLÓGICO NACIONAL (SMN). Estaciones meteorológicas. Available at: <https://smn.conagua.gob.mx/es/...> (accessed 22 October 2022).
25.
NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION (NOAA) DATABASE CLIMATE INDICES: monthly atmospheric and ocean time. Available online: <https://psl.noaa.gov/data/clim...> (accessed on 15 June 2022).
26.
LLANES C.O., NORZAGARAY C.M., GAXIOLA A., GONZÁLEZ G.G.E. Regional precipitation teleconnected with PDO‑AMO‑ENSO in northern Mexico. *Theoretical and Applied Climatology*, 140, 667, 2020. <https://doi.org/10.1007/s00704...>.
27.
TIGKAS D., VANGELIS H., PROUTSOS N., TSAKIRIS G. Incorporating aSPI and eRDI in Drought Indices Calculator (DrinC) software for agricultural drought characterisation and monitoring. *Hydrology*, 9 (6), 100, 2022. <https://doi.org/10.3390/hydrol...>.
28.
DOS SANTOS S., BEZERRA C.J.J., TÔRRES R.D., DOS SANTOS S.F.D. Climatology and significant trends in air temperature in Alagoas, Northeast Brazil. *Theoretical and Applied Climatology*, 151, 1805, 2023. <https://doi.org/10.1007/s00704...>.
29.
SIDIQI M., KASIVISWANATHAN K.S., SCHEYTT T., DEVARAJ S. Assessment of meteorological drought under climate change in the Kabul River Basin, Afghanistan. *Atmosphere*, 14 (3), 570, 2023. <https://doi.org/10.3390/atmos1...>.
30.
CONSEJO PARA EL DESARROLLO ECONÓMICO DE SINALOA (CODESIN). Sinaloa en números: agricultura en Sinaloa al 2020. Reporte 29 del 2021, 3 p., 2021. <https://sinaloaennumeros.codes...>.
31.
MONTAÑO L.Y., SOTO J.M., PÁEZ O.F. Seabed morphodynamics of a coastal lagoon of the Gulf of California. *Environmental Fluid Mechanics*, 23, 533, 2023. <https://doi.org/10.1007/s10652...>.
32.
AUSTIN C.P., VAN BUUREN S. The effect of high prevalence of missing data on estimation of the coefficients of a logistic regression model when using multiple imputation. *BMC Medical Research Methodology*, 22 (196), 1, 2022. <https://doi.org/10.1186/s12874...> PMid:35850734 PMCid:PMC9290209.
33.
AIEB A., MADANI K., SCARPA M., BONACCORSO B., LEFSIH K. A new approach for processing climate missing databases applied to daily rainfall data in Soummam watershed, Algeria. *Heliyon*, 5 (2), e01247, 2019. <https://doi.org/10.1016/j.heli...> PMid:30886916 PMCid:PMC6384304.
34.
JOHNSON M. Lose Something? Ways to Find Your Missing Data. In *Proceedings of the Houston Center for Quality of Care and Utilization Studies Professional Development Series*, 17 September, 2003.
35.
MCKEE T.B., DOESKEN N.J., KLEIST J. The Relationship of Drought Frequency and Duration to Time Scales. In *Proceedings of the Eighth Conference on Applied Climatology*, Anaheim, CA, USA, 17–22 January, p. 179, 1993.
36.
AMANUEL D.J., ADDISU Y.T., TUJUBA K.T. Spatiotemporal analysis of drought in Oromia regional state of Ethiopia over the period 1989 to 2019. *Natural Hazards*, 117, 1569, 2023. <https://doi.org/10.1007/s11069...>.
37.
XU H., WU M. First estimation of county‑based green water availability and implications for agriculture and bioenergy production in the United States. *Water*, 10 (2), 148, 2018. <https://doi.org/10.3390/w10020...>.
38.
JAVED T., ZHANG J., BHATTARAI N., SHA Z., RASHID S., YUN B., AHMAD S., HENCHIRI M., KAMRAN M. Drought characterization across agricultural regions of China using standardized precipitation and vegetation water supply indices. *Journal of Cleaner Production*, 313, 127866, 2021. <https://doi.org/10.1016/j.jcle...>.
39.
EDWARDS C., MCKEE T., DOESKEN N., KLEIST J. Historical Analysis of Drought in the United States. 7th Conference on Climate Variations, 77th AMS Annual Meeting, 1997.
40.
ABRAMOWITZ M., STEGUN I.A. *Handbook of Mathematical Functions*. Dover Publications, New York, 361 p., 1965.
41.
HARGREAVES G.H., SAMANI Z.A. Reference crop evapotranspiration from temperature. *Applied Engineering in Agriculture*, 1 (2), 96, 1985. <https://doi.org/10.13031/2013....>.
42.
HARGREAVES G.L., HARGREAVES G.H., RILEY J.P. Irrigation water requirements for Senegal River Basin. *Journal of Irrigation and Drainage Engineering*, 111 (3), 265–275, 1985. <https://doi.org/10.1061/(ASCE)...)>.
43.
HARGREAVES G.H., ALLEN R.G. History and evaluation of Hargreaves evapotranspiration equation. *Journal of Irrigation and Drainage Engineering*, 129 (1), 53–63, 2003. <https://doi.org/10.1061/(ASCE)...)>.
44.
RASSOUL Z.A., REZA M.M. Evaluation of changes in RDIst index affected by different potential evapotranspiration calculation methods. *Water Resources Management*, 31, 4981, 2017. <https://doi.org/10.1007/s11269...>.
45.
HAMAL K., SHARMA S., KHADKA N., HAILE G.G., JOSHI B.B., XU T., DAWADI B. Assessment of drought impacts on crop yields across Nepal during 1987–2017. *Meteorological Applications*, 27, e1950, 2020. <https://doi.org/10.1002/met.19...>.
46.
VANGELIS H., TIGKAS D., TSAKIRIS G. The effect of PET method on Reconnaissance Drought Index (RDI) calculation. *Journal of Arid Environments*, 88, 130, 2013. <https://doi.org/10.1016/j.jari...>.
47.
DEVORE J.L. *Probability and Statistics for Engineering and the Sciences*. 8th ed., Brooks/Cole, Cengage Learning, 776 p., 2010.
48.
NXUMALO G., BASHIR B., ALSAFADI K., BACHIR H., HARSÁNYI E., ARSHAD S., MOHAMMED S. Meteorological drought variability and its impact on wheat yields across South Africa. *International Journal of Environmental Research and Public Health*, 19 (24), 16469, 2022. <https://doi.org/10.3390/ijerph...> PMid:36554349 PMCid:PMC9779276.
49.
WANG L., CHEN W., ZHOU W., HUANG G. Understanding and detecting super‑extreme droughts in Southwest China through an integrated approach and index. *Quarterly Journal of the Royal Meteorological Society*, 142, 529, 2015. <https://doi.org/10.1002/qj.259...>.
50.
SECRETARÍA DE MEDIO AMBIENTE Y RECURSOS NATURALES (SEMARNAT). *Glosario*, 2022. <http://dgeiawf.semarnat.gob.mx...>.
51.
KAMRUZZAMAN M., ALMAZROUI M., SALAM M.A., ANARUL H.M.MD., MIZANUR R.MD., DEB L., KUMAR K.P., ASAD U.Z.MD., TOWFIQUL I.A.B.R.MD. Spatiotemporal drought analysis in Bangladesh using the SPI and SPEI indices. *Scientific Reports*, 12, 20694, 2022. <https://doi.org/10.1038/s41598...> PMid:36450747 PMCid:PMC9712418.
52.
REN Y., ZHANG F., ZHAO C., WANG D., LI J., ZHANG J., CHENG Z. Spatiotemporal changes of extreme climate indices and their influence factors in a cold river basin in Northeast China. *Theoretical and Applied Climatology*, 152, 1285, 2023. <https://doi.org/10.1007/s00704...>.
53.
ZHANG H.C., WU Q., LI F.Y., LI H. Multitask learning based on LS‑SVR for stock forecasting. *Axioms*, 11 (6), 292, 2022b. <https://doi.org/10.3390/axioms...>.
54.
OXFORD CAMBRIDGE AND RSA (OCR). Formulae and statistical tables (ST1). <https://www.ocr.org.uk/Images/...> (accessed 22 August 2022).
55.
ORMAZA G.F.I., ESPINOZA C.M.E., ROA L.H.M. Did Schwabe cycles 19–24 influence ENSO, PDO, and AMO indexes in the Pacific and Atlantic Oceans? *Global and Planetary Change*, 217, 103928, 2022. <https://doi.org/10.1016/j.glop...>.
56.
AZUZ A.I., GONZÁLEZ C.C., CUEVAS C.A. Predicting the temporal structure of the Atlantic Multidecadal Oscillation (AMO) for agriculture management in Mexico's coastal zone. *Journal of Coastal Research*, 35 (1), 210, 2019. <https://doi.org/10.2112/JCOAST...>.
57.
CHEN Z., WU R., ZHAO Y., WANG Z. Roles of dynamic and thermodynamic effects in seasonal mean surface air temperature trends over Central Asia during 1979–2018. *Climate Dynamics*, 60, 2331, 2023. <https://doi.org/10.1007/s00382...>.
58.
WÜTHRICH M.V., MERZ M. *Statistical Foundations of Actuarial Learning and its Applications*. Springer Actuarial, 605 p., 2023. <https://doi.org/10.1007/978-3-...>.
59.
HOŁYŃSKA M., SŁUGOCKI Ł. Freshwater microcrustaceans (Copepoda: Cyclopidae) on islands: a review. *Hydrobiologia*, 850, 183, 2023. <https://doi.org/10.1007/s10750...>.
60.
REY R.D.C., DOMÍNGUEZ I., OVIEDO O.E.R. Effect of agricultural activities on surface water quality from Paramo ecosystems. *Environmental Science and Pollution Research*, 29, 83169, 2022. <https://doi.org/10.1007/s11356...> PMid:35764727 PMCid:PMC9243867.
61.
WEI J. The adoption of repeated measurement of variance analysis and Shapiro–Wilk test. *Frontiers of Medicine*, 16 (4), 659, 2022. <https://doi.org/10.1007/s11684...> PMid:35776406 PMCid:PMC9441815.
62.
MANASSEH C.O., IROHA M.N., OKERE I.K., NWAKOBY C.I., OKANYA C.O., NWONYE N., ODIDI O., INYIAMA I.O. Application of Markov chain to share price movement in Nigeria (1985–2019). *Future Business Journal*, 8 (1), 59, 2022. <https://doi.org/10.1186/s43093...>.
63.
RESTREPO B.L.F., GONZÁLEZ L.J. From Pearson to Spearman. *Revista Colombiana de Ciencias Pecuarias*, 20 (2), 183, 2007. <https://doi.org/10.17533/udea....>.
64.
LLANES C.O., NORZAGARAY C.M., MUÑOZ S.N.P., RUIZ G.R., TROYO D.E., ÁLVAREZ R.P. Hydroclimatic trends in areas with high agricultural productivity in northern Mexico. *Polish Journal of Environmental Studies*, 24 (3), 1165, 2015. <https://doi.org/10.15244/pjoes...>.
65.
SEKHAR M.S., PRASAD S.R., MAITY R. Climate change may cause oasification or desertification both: an analysis based on the spatio‑temporal change in aridity across India. *Theoretical and Applied Climatology*, 155, 1167, 2024. <https://doi.org/10.1007/s00704...>.
66.
LLANES C.O., NORZAGARAY C.M., PÉREZ G.E., GAXIOLA A., LÓPEZ R.J.S., GONZÁLEZ G.G.E. Trend analysis and historical and recent return periods of erosivity indicators in Sinaloa, Mexico. *Arabian Journal of Geosciences*, 13, 212, 2020. <https://doi.org/10.1007/s12517...>.
67.
NANDI S., BISWAS S. Spatiotemporal distribution of groundwater drought using GRACE‑based satellite estimates: a case study of lower Gangetic Basin, India. *Environmental Monitoring and Assessment*, 196, 151, 2024. <https://doi.org/10.1007/s10661...> PMid:38225529.
68.
SOUSA L.J.J., RODRIGUES B.F.N., CAVALCANTE P.E., MARIANO I.A.L. Rainfed crops forecasting in the semiarid region under scenarios of rainfall instability in Ceará, Brazil. *Journal of Agricultural Science and Technology*, A12, 43, 2022. <https://doi.org/10.17265/2161-...>.
69.
ATASHI Y.S.S., MOTAMEDVAZIRI B., ZEYNALABEDIN H.S., AHMADI H. Reciprocal analysis of groundwater potentiality and vulnerability modeling in the Bahabad Plain, Iran. *Environmental Science and Pollution Research*, 30, 39586, 2023. <https://doi.org/10.1007/s11356...> PMid:36596973.
70.
BANDAK S., REZA S.A.M.N., ZEINALI E., BANDAK I. Effects of superabsorbent polymer A200 on soil characteristics and rainfed winter wheat growth (*Triticum aestivum* L.). *Arabian Journal of Geosciences*, 14 (712), 1, 2021. <https://doi.org/10.1007/s12517...>.
71.
HAQUE M.A., ZHU X., DUNKERLEY D., HENLEY J.B. Observed meteorological drought trends in Bangladesh identified with the Effective Drought Index (EDI). *Agricultural Water Management*, 255, 107001, 2021. <https://doi.org/10.1016/j.agwa...>.
72.
HUANG C., LIU H., LI H., ZUO J., WANG R. Combined effects of ENSO and PDO on activity of major hurricanes in the eastern North Pacific. *Climate Dynamics*, 62, 1, 2023. <https://doi.org/10.1007/s00382...>.
| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
