Parametric and nonparametric methods for analyzing the trend of extreme events

Jamali, Mohammad; Eslamian, Saeid

doi:10.1016/b978-0-12-821961-4.00010-5

Cited by 2 publications

(2 citation statements)

References 53 publications

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“…These metrics collectively indicate that the CHIRPS dataset closely mirrors the actual rainfall measurements recorded at ground stations across Rwanda, capturing the wide variance in rainfall with a high level of precision. The strong positive correlation suggests that the CHIRPS dataset effectively replicates the trends and fluctuations in rainfall observed during this critical agricultural season as was previously reported [36,37]. The RMSE, when considered against the backdrop of the broad range of rainfall amounts, points to a relatively minor average deviation from the station data, underscoring the dataset's reliability for practical applications.…”

Section: Evaluation Of Chirps Dataset With Station Datasupporting

confidence: 70%

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Investigation of the Historical Trends and Variability of Rainfall Patterns during the March–May Season in Rwanda

Uwizewe,

Jianping,

Habumugisha

et al. 2024

Atmosphere

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This study explores the spatiotemporal variability and determinants of rainfall patterns during the March to May (MAM) season in Rwanda, incorporating an analysis of teleconnections with oceanic–atmospheric indices over the period 1983–2021. Utilizing the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset, the study employs a set of statistical tools including standardized anomalies, empirical orthogonal functions (EOF), Pearson correlation, the Mann–Kendall (MK) trend test, and Sen’s slope estimator to dissect the intricacies of rainfall variability, trends, and their association with large-scale climatic drivers. The findings reveal a distinct southwest to northwest rainfall gradient across Rwanda, with the MK test signaling a decline in annual precipitation, particularly in the southwest. The analysis for the MAM season reveals a general downtrend in rainfall, attributed in part to teleconnections with the Indian Ocean Sea surface temperatures (SSTs). Notably, the leading EOF mode for MAM rainfall demonstrates a unimodal pattern, explaining a significant 51.19% of total variance, and underscoring the pivotal role of atmospheric dynamics and moisture conveyance in shaping seasonal rainfall. The spatial correlation analysis suggests a modest linkage between MAM rainfall and the Indian Ocean Dipole, indicating that negative (positive) phases are likely to result in anomalously wet (dry) conditions in Rwanda. This comprehensive assessment highlights the intricate interplay between local rainfall patterns and global climatic phenomena, offering valuable insights into the meteorological underpinnings of rainfall variability during Rwanda’s critical MAM season.

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Section: Evaluation Of Chirps Dataset With Station Datasupporting

confidence: 70%

“…previously reported [36,37]. The RMSE, when considered against the backdrop of the broad range of rainfall amounts, points to a relatively minor average deviation from the station data, underscoring the dataset's reliability for practical applications.…”

Section: Evaluation Of Chirps Dataset With Station Datamentioning

confidence: 72%