O E Adeyeri
I am a researcher and educator specializing in hydroclimatological studies and modelling. With a comprehensive understanding of the latest advancements in hydrological and climate models, vulnerability evaluation, remote sensing, GIS, and data visualization tools, I bring state-of-the-art knowledge to my work. With proficiency in managing large datasets and implementing multi-target heuristic streamlining techniques, I can tackle complex hydrological and climatological challenges.
Throughout my career, I have collaborated with international teams of scientists, gaining valuable experience in interdisciplinary research environments. This exposure has enhanced my ability to effectively communicate ideas to diverse audiences, enabling me to engage with and present to numerous gatherings of people.
My expertise lies in hydrological and climate extremes studies, modelling, and applying cutting-edge tools and techniques. I am passionate about advancing our understanding of hydrological processes, mitigating climate extremes, and contributing to the development of innovative solutions in this field.
Phone: +2347038875025
Throughout my career, I have collaborated with international teams of scientists, gaining valuable experience in interdisciplinary research environments. This exposure has enhanced my ability to effectively communicate ideas to diverse audiences, enabling me to engage with and present to numerous gatherings of people.
My expertise lies in hydrological and climate extremes studies, modelling, and applying cutting-edge tools and techniques. I am passionate about advancing our understanding of hydrological processes, mitigating climate extremes, and contributing to the development of innovative solutions in this field.
Phone: +2347038875025
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we demonstrate the effectiveness of the multivariate bias correction technique in facilitating precise WS representation while ensuring robust water budget closure. Historical data indicate seasonal changes, where
forested basins exhibit a WS surplus in the December-January-February season, with a reversal in the June-July-August-September season. Non-forested basins display varied patterns influenced by geographical loca-
tion and land use type. Future projections indicate increased June-July-August-September deficits in most Southern Hemisphere basins under the middle-road (SSP 245) scenario and wetter December-January-February conditions under the regional rivalry (SSP 370) scenario. Weather and climate systems governing WS vary by season and basin, resulting in inconsistent moisture intake into basins. These findings underscore
the intricate interplay between moisture transport, land characteristics, and the resulting WS, highlighting the need to understand these complex interactions for effective regional water resource management strategies in changing climates.
potential impacts on groundwater, environmental flows, as well as increase social inequalities (limited access to water by the poor), among a range of other issues. Understanding the influence of global climate on groundwater systems is thus critical to help reshape global water markets through policies underpinned by the knowledge of climatic processes driving the water cycle and freshwater supply. The main aim of this study is to improve understanding of the influence of climate variability on global groundwater using statistical methods (e.g., multi- linear regression and wavelet analyses). The response of groundwater to climate variability are assessed and the feasibility of identifying climatic hotspots of groundwater-climate interactions are explored (2003-2017).
Generally, climate variability plays a major role in the distribution of groundwater recharge, evidenced in the groundwater-rainfall relationship (r ranging from 0.6 to 0.8 with lags of 1 – 5 months) in several regions (Amazon and Congo basins, West Africa, and south Asia). Some of the areas where no relationship exists coincide with major regional aquifer systems (e.g., Nubian sand stone in north Africa) in arid domains with fossil groundwater.
Our results also show that groundwater fluxes across the world are driven by global climate teleconnections.
precipitation classes in the SRYZ. The precipitation was classified into three precipitation classes: light precipitation (0–5 mm, 5–10 mm), moderate precipitation (10–15 mm, 15–20 mm, 20–25 mm), and heavy precipitation (>25 mm). The year 1998 was detected as a changing year using the Pettitt test in the precipitation time series; therefore, the time series was divided into three scenarios: Scenario-R
(1961–2016), the pre-change point (Scenario-I; 1961–1998), and the post-change point (Scenario-II; 1999–2016). Observed annual precipitation amounts in the SRYZ during Scenario-R and Scenario-I significantly increased by 13.63 mm/decade and 48.8 mm/decade, respectively. The same increasing trend was evident in seasonal periods. On a daily scale, light precipitation (0–5 mm) covered most of the days during the entire period, with rainy days accounting for 83.50%, 84.5%, and 81.30%.
These rainy days received up to 40%, 41%, and 38% of the annual precipitation during Scenario-R, Scenario-I, and Scenario-II, respectively. Consequently, these key findings of the study will be helpful in basin-scale water resources management.
and a 10 km resolution gridded observation data from Princeton University (PGF) for the period 1979 and 2015.
The data, analysed at the annual and seasonal (wet and dry seasons) timescales, were subjected to Mann-Kendal including modified Mann-Kendall trend test after testing for autocorrelation. Test for homogeneity was performed on the data using Pettitt’s, Buishand’s, Standard Normal Homogeneity Test and van Belle and Hughes’ test. An overall homogenous trend of rainfall series was observed in the basin for all the seasons considered using van Belle and Hughes’ homogeneity trend test. The global rainfall trend increases in the dry season and decreases in the annual
and wet seasons for the period of study. Pettitt’s homogeneity test for the annual and wet season rainfall series showed that change points were detected in the year 2005 for nine grids out of fifteen over the basin at 5% significant level. This study therefore, shows the importance of understanding the spatial and temporal distribution and trends in rainfall for effective planning and management of water resources in the basin.
curve that optimally ts the exponential growth is between 1- and 53-time units with reproduction number estimate of 1.60 [1.58; 1.62] at 95% con dence interval. However, this optimal reproduction number estimate is different from the default reproduction number estimate. Using the MCMC approach, the correlation coe cients between the observed and forecasted incidence, cumulative death and cumulative confirmed cases are 0.66, 0.92 and 0.90 respectively. The projections till December shows values
approaching 1,000,000, 120,000 and 3,000,000 respectively. Therefore, timely intervention and effective preventive measures are immediately needed to mitigate a full-scale epidemic in the country.
Study focus: The GR5J hydrological model parameters are calibrated using six optimization methods i.e. Local Optimization-Multi Start (LOMS), the Differential Evolution (DE), the Multi-objective Particle the Swarm Optimization (MPSO), the Memetic Algorithm with Local Search
Chains (MALS), the Shuffled Complex Evolution-Rosenbrock’s function (SCE-R), and the Bayesian Markov Chain Monte Carlo (MCMC) approach. Three combined objective functions i.e. Root Mean Square Error, Nash- Sutcliffe efficiency, Kling-Gupta efficiency are applied. The calibration
process is divided into two separate episodes (1974–2000 and 1980–1995) so as to ascertain the robustness of the calibration approaches. Runoff simulation results are analysed with a time-
frequency wavelet transform.
New hydrological insights for the region: For calibration and validation stages, all optimization methods simulate the base flow and high flow spells with a satisfactory level of accuracy. For calibration period, MCMC underestimate it by -0.07 mm/day. The performance evaluation shows
that MCMC has the highest values of mean absolute error (0.28) and mean square error (0.40) while LOMS and MCMC record a low volumetric efficiency of 0.56. In all cases, the DE and the
SCE-R methods perform better than others. The combination of multi-objective functions and multi-optimization techniques improve the model’s parameters stability and the algorithms’ optimization to represent the runoff in the basin.
we demonstrate the effectiveness of the multivariate bias correction technique in facilitating precise WS representation while ensuring robust water budget closure. Historical data indicate seasonal changes, where
forested basins exhibit a WS surplus in the December-January-February season, with a reversal in the June-July-August-September season. Non-forested basins display varied patterns influenced by geographical loca-
tion and land use type. Future projections indicate increased June-July-August-September deficits in most Southern Hemisphere basins under the middle-road (SSP 245) scenario and wetter December-January-February conditions under the regional rivalry (SSP 370) scenario. Weather and climate systems governing WS vary by season and basin, resulting in inconsistent moisture intake into basins. These findings underscore
the intricate interplay between moisture transport, land characteristics, and the resulting WS, highlighting the need to understand these complex interactions for effective regional water resource management strategies in changing climates.
potential impacts on groundwater, environmental flows, as well as increase social inequalities (limited access to water by the poor), among a range of other issues. Understanding the influence of global climate on groundwater systems is thus critical to help reshape global water markets through policies underpinned by the knowledge of climatic processes driving the water cycle and freshwater supply. The main aim of this study is to improve understanding of the influence of climate variability on global groundwater using statistical methods (e.g., multi- linear regression and wavelet analyses). The response of groundwater to climate variability are assessed and the feasibility of identifying climatic hotspots of groundwater-climate interactions are explored (2003-2017).
Generally, climate variability plays a major role in the distribution of groundwater recharge, evidenced in the groundwater-rainfall relationship (r ranging from 0.6 to 0.8 with lags of 1 – 5 months) in several regions (Amazon and Congo basins, West Africa, and south Asia). Some of the areas where no relationship exists coincide with major regional aquifer systems (e.g., Nubian sand stone in north Africa) in arid domains with fossil groundwater.
Our results also show that groundwater fluxes across the world are driven by global climate teleconnections.
precipitation classes in the SRYZ. The precipitation was classified into three precipitation classes: light precipitation (0–5 mm, 5–10 mm), moderate precipitation (10–15 mm, 15–20 mm, 20–25 mm), and heavy precipitation (>25 mm). The year 1998 was detected as a changing year using the Pettitt test in the precipitation time series; therefore, the time series was divided into three scenarios: Scenario-R
(1961–2016), the pre-change point (Scenario-I; 1961–1998), and the post-change point (Scenario-II; 1999–2016). Observed annual precipitation amounts in the SRYZ during Scenario-R and Scenario-I significantly increased by 13.63 mm/decade and 48.8 mm/decade, respectively. The same increasing trend was evident in seasonal periods. On a daily scale, light precipitation (0–5 mm) covered most of the days during the entire period, with rainy days accounting for 83.50%, 84.5%, and 81.30%.
These rainy days received up to 40%, 41%, and 38% of the annual precipitation during Scenario-R, Scenario-I, and Scenario-II, respectively. Consequently, these key findings of the study will be helpful in basin-scale water resources management.
and a 10 km resolution gridded observation data from Princeton University (PGF) for the period 1979 and 2015.
The data, analysed at the annual and seasonal (wet and dry seasons) timescales, were subjected to Mann-Kendal including modified Mann-Kendall trend test after testing for autocorrelation. Test for homogeneity was performed on the data using Pettitt’s, Buishand’s, Standard Normal Homogeneity Test and van Belle and Hughes’ test. An overall homogenous trend of rainfall series was observed in the basin for all the seasons considered using van Belle and Hughes’ homogeneity trend test. The global rainfall trend increases in the dry season and decreases in the annual
and wet seasons for the period of study. Pettitt’s homogeneity test for the annual and wet season rainfall series showed that change points were detected in the year 2005 for nine grids out of fifteen over the basin at 5% significant level. This study therefore, shows the importance of understanding the spatial and temporal distribution and trends in rainfall for effective planning and management of water resources in the basin.
curve that optimally ts the exponential growth is between 1- and 53-time units with reproduction number estimate of 1.60 [1.58; 1.62] at 95% con dence interval. However, this optimal reproduction number estimate is different from the default reproduction number estimate. Using the MCMC approach, the correlation coe cients between the observed and forecasted incidence, cumulative death and cumulative confirmed cases are 0.66, 0.92 and 0.90 respectively. The projections till December shows values
approaching 1,000,000, 120,000 and 3,000,000 respectively. Therefore, timely intervention and effective preventive measures are immediately needed to mitigate a full-scale epidemic in the country.
Study focus: The GR5J hydrological model parameters are calibrated using six optimization methods i.e. Local Optimization-Multi Start (LOMS), the Differential Evolution (DE), the Multi-objective Particle the Swarm Optimization (MPSO), the Memetic Algorithm with Local Search
Chains (MALS), the Shuffled Complex Evolution-Rosenbrock’s function (SCE-R), and the Bayesian Markov Chain Monte Carlo (MCMC) approach. Three combined objective functions i.e. Root Mean Square Error, Nash- Sutcliffe efficiency, Kling-Gupta efficiency are applied. The calibration
process is divided into two separate episodes (1974–2000 and 1980–1995) so as to ascertain the robustness of the calibration approaches. Runoff simulation results are analysed with a time-
frequency wavelet transform.
New hydrological insights for the region: For calibration and validation stages, all optimization methods simulate the base flow and high flow spells with a satisfactory level of accuracy. For calibration period, MCMC underestimate it by -0.07 mm/day. The performance evaluation shows
that MCMC has the highest values of mean absolute error (0.28) and mean square error (0.40) while LOMS and MCMC record a low volumetric efficiency of 0.56. In all cases, the DE and the
SCE-R methods perform better than others. The combination of multi-objective functions and multi-optimization techniques improve the model’s parameters stability and the algorithms’ optimization to represent the runoff in the basin.