Simulated impact of global warming on extreme rainfall events over Cameroon during the 21st century

Chouto, Steven; Djuidje, Flore K.; Matsaguim, Cédric N.; Fotso‐Nguemo, Thierry C.; Nghonda, Jean Pierre; Vondou, Derbetini A.

doi:10.1002/wea.3867

Cited by 13 publications

(8 citation statements)

References 15 publications

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“…It follows a spatio-temporal variability of precipitation as well as a northward migration of dry isohyets. These observations are in agreement with findings in [8]. A systematic low SDII was obtained in the northernmost part of Cameroon with values below 7 mm.…”

Section: Spatial Variability Of Extreme Indicessupporting

confidence: 93%

“…A number of recent studies [6] highlight the necessity for consideration of using dynamically downscaled regional climate models (RCMs), such as those from the Coordinated Regional Climate Downscaling Experiment Program (CORDEX) to analyze extreme trends. Results of [7] and [8] revealed that the RCMs used in their study mimic quite well the spatial distribution of precipitation extremes. Rainfall extreme changes have been explored in many regions in Africa [9]; they showed some significant trends in heavy precipitation over Central Africa.…”

Section: Introductionmentioning

confidence: 90%

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Trends and Interannual Variability of Extreme Rainfall Indices over Cameroon

et al. 2021

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Central African citizens are highly vulnerable to extreme hydroclimatic events due to excess precipitation or to dry spells. This study makes use of CHIRPS precipitation data gridded at 0.05° × 0.05° resolution and extended from 1981 to 2019 to analyze spatial variabilities and trends of six extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) over Cameroon. They are the number of wet days (RR1), the simple daily intensity index (SDII), the annual total precipitation from days greater than the 95th percentile (R95ptot), the maximum number of consecutive wet days (CWD), the maximum number of consecutive dry days (CDD), the number of very heavy rainfall (RR20). The standard precipitation index (SPI) time series were also examined in the five agro-climatic regions of the domain. The pattern of annual precipitation was first checked over the entire domain. We obtain a well-known pattern showing a decreased precipitation northward with the highest values around the Atlantic Ocean coast. The analysis shows that all indices represent patterns approximately similar to that of annual rainfall except CDD where the spatial south-north gradient is reversed. RR20 shows the lowest spatial variability. Trend study of RR1 indicates negative values south of the domain and predominated positive values in the northern part, where CDD, on the contrary, shows a decreased trend. The highest trends are observed in the northernmost area for CWD and around the coast for SDII and R95ptot. SPI time series indicate an alternative dry and wet period and the years between 1990 and 2000 witnessed more annual wet conditions. Such a study is very important in this domain where variabilities of climatic components are very high due to climate change impact and diversified relief. The results can serve as a reference for agricultural activity, hydropower management, civil engineering, planning of economic activities and can contribute to the understanding of the climate system in Cameroon.

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Section: Spatial Variability Of Extreme Indicessupporting

confidence: 93%

Section: Introductionmentioning

confidence: 90%

Trends and Interannual Variability of Extreme Rainfall Indices over Cameroon

et al. 2021

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“…Data limitations and observation station sparsity are still a commonly cited concern for many researchers in the years 2013-2020 (see Tables S1-S11) for a full list of relevant publications citing this limitation). The lack of a spatially extensive, high-temporal resolution observation network has limited the attempts to evaluate the impacts of climate change in several societal domains when utilizing simulation outputs from Global Climate Model (GCM), often embedded in the Coupled Model Intercomparison Project (CMIP); as well as Regional Climate Model (RCM), carried out within the framework of the COordinated Regional Climate Downscaling Experiment (CORDEX) initiative [29,33,34,[42][43][44][45]. The latter authors noted that the lack of precipitation-related variables in mountainous regions may lead to interpolation errors.…”

Section: Resultsmentioning

confidence: 99%

“…Information at a regional scale is highly desirable, particularly across the African continent, for practical planning of local issues, such as rain-fed agriculture, water resources availability, and flood management. Often, challenges in acquiring adequate data for the modelling task at hand and the spatial distribution of the data may render certain analyses impossible; for instance, on accounting for small geographic areas with high topographical variability, see [21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Towards Resolving Challenges Associated with Climate Change Modelling in Africa

et al. 2022

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Climate change is a significant concern impacting food security, agricultural reform, disease transmission, and disruption to human, plant, and animal ecosystems, along with a host of additional consequences, ultimately affecting the quality of life and the livelihoods of the global population. African-based research aims to better understand the impact of climate change on nature and on different aspects of humanity, as well as improve forecasting for greater economic potential. However, researchers often encounter various challenges and obstacles. Here, we conducted a bibliographic analysis and interpretation of relevant climate change peer-reviewed research articles related to the African continent. From this analysis, challenges associated with climate change modelling in Africa were identified. Primarily, the lack of an extensive observational network and technological limitations hinder modelling efforts. Additionally, an apparent pull of scientists away from African institutions to institutions further afield was observed. Novel solutions to these challenges are proffered. Finally, we highlight how the German Deutscher Akademischer Austauschdienst (DAAD) Climate Research for Alumni and Postdocs in Africa (climapAfrica) program is contributing towards resolving these challenges.

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“…The area featuring high and very high agricultural drought hazards are distributed in most parts of the study domain [10]. In addition, the total wet-day rainfall amount above the 95th percentile is projected to consistently increase (by about 10-15%) during the first half of the century in the study area [20]. Based on this analysis, there is a clear and present need to aggregate cotton yield and climate data from different regions, perhaps with more detailed management information, to provide a much-needed observational constraint on projections of both climate change and management impacts on future food security.…”

Section: Discussionmentioning

confidence: 99%

Contributions of Seasonal Rainfall to Recent Trends in Cameroon’s Cotton Yields

et al. 2021

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Cotton yields in the Sudano-Sahelian region contribute to food security through their role in agricultural productivity. Daily precipitation data and cotton yield data were synthesized from nine agricultural regions obtained from the “Société de Développement du Coton (SODECOTON)”. The following seasonal rainfall indices—from Cameroon’s cotton zone—were mapped with geographic information systems for spatial analysis: wet season onset and retreat date, rainfall amount, number of rainy days, rainfall intensity (SDII), heavy-rainfall events (R95p), consecutive dry days (CDD), annual highest daily precipitation (Rx1day) and number of very heavy precipitation days (R20mm). Linear regressions were used as statistical tools for analysis. The strongest relationships were observed between cotton yields and the heavy-rainfall events, closely followed by seasonal rainfall amount. An increase in consecutive dry days (CDD) and heavy events, and a decreased seasonal rainfall amount, have a negative impact on cotton yield trends. Overall, the critical breakpoint analysis between cotton yields and all rainfall indices showed that the cotton yield was particularly negatively impacted before a 251 retreat date, 591 mm seasonal rainfall amount and 33 rainy days. By contrast, an onset date, rainfall intensity, heavy rainfall, CDD, Rx1day and R20mm of 127, 12.5 mm·day−1, 405 mm, 27 days, 67 mm and 22 days, respectively, were identified for an optimum cotton yield. These results can be used as information for agricultural activity and management, civil planning of economic activities and can also contribute to furthering our understanding of the management impacts on future food security.

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Simulated impact of global warming on extreme rainfall events over Cameroon during the 21st century

Cited by 13 publications

References 15 publications

Trends and Interannual Variability of Extreme Rainfall Indices over Cameroon

Trends and Interannual Variability of Extreme Rainfall Indices over Cameroon

Towards Resolving Challenges Associated with Climate Change Modelling in Africa

Contributions of Seasonal Rainfall to Recent Trends in Cameroon’s Cotton Yields

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