Browsing by Author "Ayugi, Brian Odhiambo"

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Amplification of compound hot-dry extremes and associated population exposure over East Africa
(Climatic Change, 2024-09-09) Ayugi, Brian Odhiambo; Onyutha, Charles; Zhu, Huanhuan; Babousmail, Hassen; Chung, Eun-Sung; Sian, Kenny Thiam Choy Lim Kam
Quantifying the vulnerability of population to multi-faceted climate change impacts on human well-being remains an urgent task. Recently, weather and climate extremes have evolved into bivariate events that heighten climate risks in unexpected ways. To investigate the potential impacts of climate extremes, this study analyzes the frequency, magnitude, and severity of observed and future compound hot-dry extremes (CHDEs) over East Africa. The CHDE events were computed from the observed precipitation and maximum temperature data of the Climatic Research Unit gridded Timeseries version five (CRU TS4.05) and outputs of climate models of Coupled Model Intercomparison Project Phase 6 (CMIP6). In addition, this study quantifies the population exposure to CHDE events based on future population density datasets under two Shared Socioeconomic Pathways (SSPs). Using the 75th/90th and 25th/10th percentile of precipitation and temperature as threshold to define severe and moderate events, the results show that the East African region experienced multiple moderate and severe CHDE events during the last twenty years. Based on a weighted multi-model ensemble, projections indicate that under the SSP5-8.5 scenario, the frequency of moderate CHDE will double, and severe CHDE will be 1.6 times that of baseline (i.e., an increase of 60%). Strong evidence of an upward trajectory is noted after 2080 for both moderate and severe CHDE. Southern parts of Tanzania and northeastern Kenya are likely to be the most affected, with all models agreeing (signal-to-noise ratio, SNR > 1), indicating a likely higher magnitude of change during the mid- and far-future. Consequentially, population exposure to these impacts is projected to increase by up to 60% for moderate and severe CHDEs in parts of southern Tanzania. Attribution analysis highlights that climate change is the primary driver of CHDE exposure under the two emission pathways. The current study underscores the urgent need to reduce CO2 emissions to prevent exceeding global warming thresholds and to develop regional adaptation measures.
Drought severity across Africa: a comparative analysis of multi-source precipitation datasets
(Springer, 2024-04) Lim Kam Sian, Kenny Thiam Choy; Onyutha, Charles; Ayugi, Brian Odhiambo; Njouenwet, Ibrahim; Ongoma, Victor
An accurate analysis of climate extremes is essential for impact assessment and devising appropriate adaptation measures. There is an urgent need to assess precipitation products in capturing the increasing occurrence of climate extremes. This study evaluates the ability of 20 observational datasets, including gauge-based, satellite-based and reanalyses, in representing different drought severity (moderate, severe and extreme drought) over Africa and its nine sub-regions at varying time scales (3-, 6- and 12-months) during 1983–2014. Drought is represented using the Standardized Precipitation Index (SPI). The results demonstrate that while most datasets are suitable for drought studies over the continent, the African Rainfall Climatology version 2 (ARC2) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Records (PERSIANN_CDR_v1r1) are less fitted for such investigations. Moreover, regions such as the Sahara (SAH), Central Africa (CAF) and North Eastern Africa (NEAF) show a larger disparity among the datasets, requiring more caution when selecting a dataset for use in such areas. Generally, the datasets present low agreement toward the lower end of the range (5–30%) because the individual datasets estimate varying drought severities at different grids and months. This is observed in the coefficient of variation of 20–25% of the datasets falling outside the ± 1 standard deviation range. Therefore, using an ensemble to represent the datasets remains an indispensable tool. The datasets present better agreement in the timing of drought events than the spatial distribution. The findings provide valuable insights into the complexity of drought assessment using diverse precipitation datasets. Furthermore, the results highlight the significance of considering spatial and temporal dimensions, as datasets may capture drought events at varying locations and times, revealing subtle variations in drought impact.
Sensitivity of streamflow to changing rainfall and evapotranspiration in catchments across the Nile Basin
(MDPI, 2024-11-25) Onyutha, Charles; Ayugi, Brian Odhiambo; Sian, Kenny Thiam Choy Lim Kam; Babaousmail, Hassen; Arineitwe, Wenseslas; Akobo, Josephine Taata; Chelangat, Cyrus; Mubialiwo, Ambrose
This research focuses on the complex dynamics governing the sensitivity of streamflow to variations in rainfall and potential evapotranspiration (PET) within the Nile basin. By employing a hydrological model, our study examines the interrelationships between meteorological variables and hydrological responses across six catchments (Blue Nile, El Diem, Kabalega, Malaba, Mpanga, and Ribb) and explores the intricate balance between rainfall, PET, and streamflow. Nash Sutcliffe Efficiency (NSE) for calibration of the hydrological model ranged from 0.636 (Ribb) to 0.831 (El Diem). For validation, NSE ranged from 0.608 (Ribb) to 0.811 (Blue Nile). With rainfall kept constant while PET was increased by 5%, the streamflows of the Blue Nile, El Diem, Kabalega, Malaba, Mpanga, and Ribb decreased by 7.00, 5.08, 2.49, 4.10, 1.84, and 7.67%, respectively. With the original PET data unchanged, increasing rainfall of the Blue Nile, El Diem, Kabalega, Malaba, Mpanga, and Ribb by 5% led to an increase in streamflow by 9.02, 9.87, 5.38, 4.34, 6.58, and 8.32%, respectively. The research reveals that the rate at which a catchment losing water to the atmosphere (determined by PET) substantially influences its drying rate. Utilizing linear models, we demonstrate that the surplus rainfall available for increasing streamflow (represented by model intercepts) amplifies with higher rainfall intensities. This highlights the pivotal role of rainfall in shaping catchment water balance dynamics. Moreover, our study stresses the varied sensitivities of catchments within the basin to changes in PET and rainfall. Catchments with lower PET exhibit heightened responsiveness to increasing rainfall, accentuating the influence of evaporative demand on streamflow patterns. Conversely, regions with higher PET rates necessitate refined management strategies due to their increased sensitivity to changes in evaporative demand. Understanding the intricate interplay between rainfall, PET, and streamflow is paramount for developing adaptive strategies amidst climate variability. By examining these relationships, our research contributes essential knowledge for sustainable water resource management practices at both the catchment and regional scales, especially in regions susceptible to varying sensitivities of catchments to climatic conditions.