Abstract
Many climate studies in Mozambique have clearly identified signals of climate change, especially changes in the extreme temperatures. Regarding precipitation, there is still a gap on the knowledge of how it is behaving due to both internal and external factors in the climate system. In this study, we have investigated the existence of long-term correlations and trend in time series of precipitation. Two databases were used for this purpose: in situ observations along the period of 1960–2020 and the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) dataset, along the period from 1981 to 2021. We have applied the rescaled-range analysis and the detrended fluctuation analysis for long memory investigation, and the linear regression and Mann-Kendall methods for trend analysis. Results have shown the existence of long memory in precipitation in most parts of Mozambique, being stronger in the southern and central regions and weakening toward the north of the country. On the other hand, significant trend signals of precipitation were detected in some isolated areas of Mozambique, presenting an increase in some regions such as the southern part of Manica and eastern of Inhambane provinces and a decrease in other regions such as the coastal areas of Zambezia and Nampula. These findings indicate that the probability of a random occurrence of precipitation is minimal, and the observed trends are likely to continue for a long period in future. Dry land agriculture should be prepared to adapt to new precipitation regime in the regions mentioned hereof.
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Availability of data and material
In situ datasets analyzed during this study can be obtained from the National Institute of Meteorology under specific request, and CHIRPS datasets are publicly available and can freely be downloaded from the link https://data.chc.ucsb.edu/products/CHIRPS-2.0/. Code scripts and data generated from the study (gap filled station data, figures, etc.) are available from the corresponding author on reasonable request.
Notes
SARMAX(p, q)(P, Q, m) is actually an ARMA(p, q) model, except that SARMAX takes into account the seasonal component of order (P, Q, m) in the time series, and an eXogenous variable.
ARMA model is the combination of Auto-Regressive AR(p) and Moving Average MA(q) models.
CMIP5 - Coupled Model Intercomparison Project Phase 5.
RCP4.5 is an acronym designated to represent the intermediate scenario of greenhouse gas emission policy.
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Acknowledgements
The authors would like to thank the National Institute of Meteorology (INAM) and the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) for providing the climate data used in this study. A special thanks to Mr. Guelso Mauro Manjate for his kind facilitation on the acquisition process of INAM’s data.
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JLMU proposed the objectives and most of the methodology, compiled the data, performed the data analysis, generated the figures, and wrote and reviewed the manuscript. AB proposed additional methodology, commented the results, and reviewed the manuscript.
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Ussalu, J.L.M., Bassrei, A. Long memory and trend in time series of precipitation in Mozambique. Theor Appl Climatol 154, 643–659 (2023). https://doi.org/10.1007/s00704-023-04579-x
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DOI: https://doi.org/10.1007/s00704-023-04579-x