Heterogeneous Trends of Precipitation Extremes in Recent Two Decades over East Africa

Mtewele, Zacharia Florence; Xu, Xiyan; Jia, Gensuo

doi:10.1007/s13351-021-1028-8

Cited by 9 publications

(8 citation statements)

References 87 publications

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“…However, these extremes expound a reversal from more EDE to EWE during the study period. This pattern corroborates with the reported turning of downward to upward rainfall trends over East Africa (Lyon and Vigaud 2017, Walker et al 2020, Mtewele et al 2021. This would infer a relative stabilization of animal population dynamics as EDE exacerbates survival threats more than EWE.…”

Section: Discussionsupporting

confidence: 89%

“…Regionally, East Africa experiences detrimental impacts of climate variability and change, with a notable increase in the intensity and frequency of climate extremes (Nicholson 2017, Mtewele et al 2021, Omondi and Lin 2023. Moreover, East Africa reveals a continued increase in hydrological extremes throughout the 21st century (Haile et al 2020, Gebrechorkos et al 2023, and nonlinear vegetation trends and high vegetation variability in response to precipitation and temperature change (Hawinkel et al 2016, Kalisa et al 2019, Huang et al 2021.…”

Section: Introductionmentioning

confidence: 99%

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Serengeti–Masai Mara ecosystem dynamics inferred from rainfall extremes

Mtewele,

Jia,

2023

Environ. Res. Lett.

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The Serengeti–Masai Mara Ecosystem (SMME) is an iconic ecological and biodiversity resource in East Africa with a spectacular great animal migration feature. Environmental shocks like droughts, floods, and land degradation threaten the SMME’s ecological functioning. However, the spatiotemporal ecosystem dynamics during climate extremes are inadequately examined. Here we quantified water availability and vegetation changes during extreme wet (EWE) and dry (EDE) events over the SMME for wet and dry seasons in 1982–2020. We derived extreme events from seasonal mean precipitation anomaly exceeding standard deviation and applied composite and correlation techniques to assess their dynamics with animal populations and migration prospects. Wet season EWE increases vegetative and moist conditions over southern SMME, suggesting elongating migrants’ occupancy compared to normal periods and delayed northward migration. Wet season EDE significantly suppresses these features, heightening ecosystem competition and survival threats, proposing an earlier northward migration. Dry season EWE increases vegetation and water availability over northern SMME, prompting the late southward migration. Dry season EDE significantly reduces vegetation and water availability over northern SMME, suggesting an early southward migration towards regions with more vegetation and increased water availability. The SMME also experiences multiple EDE occurring in consecutive seasons, prolonging dry conditions that aggregate wildlife survival threats. Notably, two EDE prevailed consecutively from the 1993 dry season to the 1994 wet season, coupled with a population decline of wildebeest (1.2–0.9 million), buffalo (40–20 thousand), and lion (1.3–0.9 thousand). We also note a reversal from more EDE to EWE during the study period. Prevalent EWE can lessen wet and vegetative conditions distribution gradient, which is imperative for the functioning of the SMME migratory ecosystem. Our study unveiled hotspot areas of extremes-driven ecosystem changes for the sustainable SMME migratory functioning essential for framing meaningful conservation management policies under climate change.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Serengeti–Masai Mara ecosystem dynamics inferred from rainfall extremes

Mtewele,

Jia,

2023

Environ. Res. Lett.

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“…Despite the significant progress made by GCMs in recent decades, accurate simulations of precipitation remain a critical challenge for GCMs since they have considerable biases owing to coarse resolutions, imperfect boundary conditions, poor parameterizations and misrepresentation of physical processes (Li et al, 2020;Tian & Dong, 2020). The issues such as inaccuracy of simulating tropical precipitation, underestimation of extreme precipitation and mismatch between models and adjacent stations have been identified in previous CMIP5 models (Maggioni & Massari, 2018;Mtewele et al, 2021;Wehner et al, 2014). With the advancement of modeling techniques, the sixth generation of CMIP project couples more details of clouds, convection, radiation processes and they have shown significant improvements in spatial resolution, physical parameterizations, and inclusion of additional earth system processes such as nutrient limitations on the terrestrial carbon cycle and ice sheets (Z.…”

Section: Huang Et Almentioning

confidence: 99%

Investigating the Performance of CMIP6 Seasonal Precipitation Predictions and a Grid Based Model Heterogeneity Oriented Deep Learning Bias Correction Framework

Huang,

Liu,

Liu

et al. 2023

JGR Atmospheres

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Climate change is expected to alter the magnitude and spatiotemporal patterns of hydro‐climate variables such as precipitation, which has significant impacts on the ecosystem, human societies and water security. Global Climate Models are the major tools to simulate historical as well as future precipitation. However, due to imperfect model structures, parameters and boundary conditions, direct model outputs are subject to large uncertainty, which needs serious evaluation and bias correction before usage. In this study, seasonal precipitation predictions from 30 Coupled Model Inter‐comparison Project Phase 6 (CMIP6) models and Climate Research Unit observations are used to evaluate historical precipitation climatology in global continents during 1901–2014. A grid based model heterogeneity oriented Convolutional Neural Network (CNN) is proposed to correct the ensemble mean precipitation bias ratio. Besides, regression based Linear Scaling (LS), distribution based Quantile Mapping (QM) and spatial correlation CNN bias correction approaches are employed for comparison. Results of model performance evaluation indicate that generally precipitation prediction is more reliable in JJA than DJF on the global scale. Most models tend to have larger bias ratio for extreme precipitation. In addition, current CMIP6 models still have certain issues in accurate simulation of precipitation in mountainous regions and the regions affected by complex climate systems. Moreover, the proposed grid based model heterogeneity oriented CNN has better performance in ensemble mean bias correction than LS, QM, and spatial correlation CNN, which could consider the relative model performance and capture the features similar to actual climate dynamics.

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“…Floods and droughts are not only the most commonly occurring natural disasters globally (Bola et al, 2014;Yu et al, 2014;Daryanto et al, 2015;Amrit et al, 2020;Hameed et al, 2020;Global Natural Disaster Assessment Report, 2021;Han et al, 2021), but also the natural disasters causing the most significant damage to the economy, human life, and agricultural production. Floods and droughts have thus been the focus of extensive research in hydrological science, covering the occurring patterns, causes, and impacts (Samuel & Sivapalan, 2008;Apurv & Cai, 2020;Mtewele et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Identification, physical mechanisms and impacts of drought–flood abrupt alternation: a review

et al. 2023

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Climate change has led to an increase in the frequency of extreme events, such as droughts and floods. This study aims to review the literature on the newly proposed phenomenon known as drought-flood abrupt alternation (DFAA). A comprehensive summary is provided to round up the numerous approaches employed to identify DFAA events, as well as its mechanisms and impacts. To provide a reference for responding and managing the emerging intensity and frequency of DFAA events, we conclude the paper by listing the insufficiency of current research and suggesting possible future research directions. As for the impact of DFAA, besides the loss of life and property which can be caused by any natural disaster, a DFAA event severely threatens food security by making a lasting and profound impact on the land productivity through the alteration of the combining conditions of water, soil, and temperature. As for the future research directions, existing indexes developed for DFAA identification should be improved by downscaling the temporal and spatial scale, with interactions of neighboring drought and flood events taken into consideration. What’s more, to better protect human society from the losses caused by DFAA, researches on accurate DFAA prediction are encouraged.

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Heterogeneous Trends of Precipitation Extremes in Recent Two Decades over East Africa

Cited by 9 publications

References 87 publications

Serengeti–Masai Mara ecosystem dynamics inferred from rainfall extremes

Serengeti–Masai Mara ecosystem dynamics inferred from rainfall extremes

Investigating the Performance of CMIP6 Seasonal Precipitation Predictions and a Grid Based Model Heterogeneity Oriented Deep Learning Bias Correction Framework

Identification, physical mechanisms and impacts of drought–flood abrupt alternation: a review

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