Browsing by Author "Onyutha, Charles"

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African food insecurity in a changing climate: the roles of science and policy
(Wiley Online Library: Food and Energy Security, 2018-12-12) Onyutha, Charles
African population is projected to double to 2.48 billion people by 2050. The population increase poses a serious challenge of increasing food supply to meet the future demand. This challenge is compounded by climate change impacts on agriculture. In this paper, how poverty contributes to household food insecurity is explored and measures suggested to help address this challenge. To plan adaptation measures, linkages among food insecurity, poverty, and illiteracy should be considered. For the sub-Saharan Africa (SSA), adaptation (focused on poverty alleviation) should be prioritized and preferred to mitigation. Enhancement of adaptive capacity should not only be tailored toward empowerment of women but also made highly localized to household levels. Generally, efforts could be geared toward yield gap closure, addressing challenges regarding food distribution, promoting non-farm income-generating activities, and unification of government priorities in agriculture and food security. Government in each country of the SSA should ensure that governance strongly embraces transparency, accountability, and integrity otherwise as it is said a fish rots from the head down. Estimates of uncertainty in predicting future climate and their implications on expenditure related to adaptation should to always be made in an integrated way and reported to support actionable policies. To increase credibility in climate prediction especially at local scales, advances toward improving climate models (for instance by refining spatiotemporal scales, enhancing models’ capacity to reproduce observed natural variability in key climatological variables like rainfall) should be made, and this requires support from the investment in climate science. Science–policy interfacing is required in planning and implementation of measures for adapting to climate change impacts. In summary, food insecurity and persistent poverty especially in the SSA should be of direct relevance and concern at a global scale. Thus, global collaboration in science is key to achieve food security in the SSA.
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.
Analyses of precipitation and evapotranspiration changes across the lake Kyoga basin in East Africa
(MDPI: Water, 2020-04-16) Onyutha, Charles; Acayo, Grace; Nyende, Jacob
This study analyzed changes in CenTrends gridded precipitation (1961–2015) and Potential Evapotranspiration (PET; 1961–2008) across the Lake Kyoga Basin (LKB). PET was computed from gridded temperature of the Princeton Global Forcings. Correlation between precipitation or PET and climate indices was analyzed. PET in the Eastern LKB exhibited an increase (p > 0.05). March–April–May precipitation decreased (p > 0.05) in most parts of the LKB. However, September–October–November (SON) precipitation generally exhibited a positive trend. Rates of increase in the SON precipitation were higher in the Eastern part where Mt. Elgon is located than at other locations. Record shows that Bududa district at the foot of Mt. Elgon experienced a total of 8, 5, and 6 landslides over the periods 1818–1959, 1960–2009, and 2010–2019, respectively. It is highly probable that these landslides have recently become more frequent than in the past due to the increasing precipitation. The largest amounts of variance in annual precipitation (38.9%) and PET (41.2%) were found to be explained by the Indian Ocean Dipole. These were followed by precipitation (17.9%) and PET (21.9%) variance explained by the Atlantic multidecadal oscillation, and North Atlantic oscillation, respectively. These findings are vital for predictive adaptation to the impacts of climate variability on water resources.
Changes in precipitation and evapotranspiration over Lokok and Lokere catchments in Uganda
(Bulletin of Atmospheric Science and Technology volume : Springer Link, 2021-03-24) Mubialiwo, Ambrose; Chelangat, Cyrus; Onyutha, Charles
This study analysed long-term (1948–2016) changes in gridded (0.25° × 0.25°) Princeton Global Forcing (PGF) precipitation and potential evapotranspiration (PET) data over Lokok and Lokere catchments. PGF-based and station datasets were compared. Trend and variability were analysed using a nonparametric technique based on the cumulative sum of the difference between exceedance and non-exceedance counts of data. Seasonal (March-April-May (MAM), June-July-August (JJA), September-October-November (SON), December-January-February (DJF)) and annual precipitation exhibited negative trends (p < 0.05). Positive anomalies in precipitation occurred in the 1950s as well as in the early 2000s till 2016. Negative anomalies existed between 1960 and 2000. Both seasonal and annual PET mainly exhibited increasing trend with alternating positive and negative anomalies for the entire period, except in the southern region. The H0 was rejected (p < 0.05) for SON PET in the North and South of the study area. The H0 was rejected (p < 0.05) for DJF PET in the North. However, H0 was not rejected (p > 0.05) for MAM, JJA and annual PET. Positive and negative correlations were observed between PGF and station precipitation varying from one location to another. The PGF-based PET were lower than the observed PET at Kotido by about 40%. Besides, a close agreement was noticeable between PGF-based and MODIS PET from May to November. This showed the need to improve on the quality of PGF data in reproducing the observed climatic data in areas with low meteorological stations density. Nevertheless, the findings from this study are relevant for planning of predictive adaptation to the effects of climate variability on the water resources management applications. Impacts of human factors and climate change on the hydrology of the study area should be quantified in future research studies.
Combined use of graphical and statistical approaches for analyzing historical precipitation changes in the black sea region of Turkey
(MDPI: Water, 2020-03-05) Cengiz, Taner Mustafa; Tabari, Hossein; Onyutha, Charles; Kisi, Ozgur
Many statistical methods have been developed and used over time to analyze historical changes in hydrological time series, given the socioeconomic consequences of the changes in the water cycle components. The classical statistical methods, however, rely on many assumptions on the time series to be examined such as the normality, temporal and spatial independency and the constancy of the data distribution over time. When the assumptions are not fulfilled by the data, test results are not reliable. One way to relax these cumbersome assumptions and credibilize the results of statistical approaches is to make a combined use of graphical and statistical methods. To this end, two graphical methods of the refined cumulative sum of the difference between exceedance and non-exceedance counts of data points (CSD) and innovative trend analyses (ITA)-change boxes alongside the classical statistical Mann–Kendall (MK) method are used to analyze historical precipitation changes at 16 stations during 1960–2015 in the Black Sea region of Turkey. The results show a good match between the results of the graphical and statistical methods. The graphical CSD and ITA methods, however, are able to identify the hidden trends in the precipitation time series that cannot be detected using the statistical MK method.
Contribution of climatic variability and human activities to stream flow changes in the Haraz River basin, Northern Iran
(Journal of Hydro-environment Research, 2019-06) Pirnia, Abdollah; Darabi, Hamid; Choubin, Bahram; Omidvar, Ebrahim; Onyutha, Charles; Haghighi, Ali Torabi
In northern Iran’s Haraz River basin between 1975 and 2010, hydrological sensitivity, double mass curve, and Soil and Water Assessment Tool (SWAT) methods were applied to monitoring and analysing changes in stream flow brought on by climatic variability and human activities. Applied to analyse trends in annual and seasonal runoff over this period, the sequential MK test showed a sudden change point in stream flow in 1994. The study period was, therefore, divided into two sub-periods: 1975–1994 and 1995–2010. The SWAT model showed obvious changes in water resource components between the two periods: in comparison to the period of 1975–1994, sub-watershed-scale stream flow and soil moisture decreased during 1995–2010. Changes in evapotranspiration were negligible compared to those in stream flow and soil moisture. The hydrological sensitivity method indicated that climatic variability and human activities contributed to 29.86% and 70.14%, respectively, of changes in annual stream flow, while the SWAT model placed these contributions at 34.78% and 65.21%, respectively. The double mass curve method indicated the contribution of climatic variability to stream flow changes to be 57.5% for the wet season and 22.87% for the dry season, while human activities contributed 42.5% and 77.13%, respectively. Accordingly, in the face of climatic variability, measures should be developed and implemented to mitigate its impacts and maintain eco-environmental integrity and water supplies.
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.
Graphical-statistical method to explore variability of hydrological time series
(Hydrology Research, 2021-02) Onyutha, Charles
Due to increasing concern on developing measures for predictive adaptation to climate change impacts on hydrology, several studies have tended to be conducted on trends in climatic data. Conventionally, trend analysis comprises testing the null hypothesis H0 (no trend) by applying the Mann–Kendall or Spearman's rho test to the entire time series. This leads to lack of information about hidden short-durational increasing or decreasing trends (hereinafter called sub-trends) in the data. Furthermore, common trend tests are purely statistical in nature and their results can be meaningless sometimes, especially when not supported by graphical exploration of changes in the data. This paper presents a graphical-statistical methodology to identify and separately analyze sub-trends for supporting attribution of hydrological changes. The method is based on cumulative sum of differences between exceedance and non-exceedance counts of data points. Through the method, it is possible to appreciate that climate variability comprises large-scale random fluctuations in terms of rising and falling hydro-climatic sub-trends which can be associated with certain attributes. Illustration on how to apply the introduced methodology was made using data over the White Nile region in Africa. Links for downloading a tool called CSD-VAT to implement the presented methodology were provided.
Hydrodynamic modelling of floods and estimating socio‑economic impacts of floods in Ugandan River Malaba sub‑catchment
(Earth Systems and Environment : Springer link, 2022-01-01) Mubialiwo, Ambrose; Abebe, Adane; Kawo, Nafyad Serre; Ekolu, Job; Nadarajah, Saralees; Onyutha, Charles
River Malaba sub-catchment tends to experience dramatic flooding events, with several socio-economic impacts to the nearby communities, such as loss of lives and destructions of physical infrastructure. Analysis of spatiotemporal extents to which settlements, crops and physical infrastructures tend to be inundated are vital for predictive planning of risk-based adaptation measures. This paper presents a case study on flood risk assessment for Ugandan River Malaba sub-catchment. We applied the two-dimensional Hydraulic Engineering Center’s River Analysis System (2D HEC-RAS) for modelling of flooding extents. We considered extreme flow quantiles, lower and upper quantiles corresponding to the 95% confidence interval limits aimed at determining uncertainties in the flooding extents. Spatial extents of inundation on human settlement, land cover and infrastructure were analysed with respect to return periods of extreme flow quantiles. Finally, we estimated economic loss on infrastructure due to flooding. Results from the 2D HEC-RAS model were satisfactorily comparable with the results of observations. Amongst the land use types, cropland exhibited the highest vulnerability with at least 10,234.8 hectare (ha) susceptible to flooding event of 100-year return period (YRP). Inundated built-up land-use exhibited the highest vulnerability percentage increase (90%) between 2- and 100-YRP. In US Dollar, about US$ 33 million and US$ 39 million losses are estimated at 2- and 100-YRP, respectively, due to inundated rice gardens and these indicate a looming high risk of household food insecurity and poverty. Several infrastructure including 15 academic institutions, 12 health facilities, 32 worshiping places remain annually vulnerable to flooding. At least 6 km and 7 km of road network are also susceptible to flooding under extreme flows of return periods 2 and 100 years, respectively. Churches exhibited the highest economic losses of US$ 855,065 and US$ 1,623,832 at 2-YRP and 100-YRP, respectively. This study findings are relevant for planning the development of sustainable flood risk adaptation pathways given the established destructions within the sub-catchment due to flooding.
A hydrological model skill score and revised R-squared
(Hydrology Research, 2022-01-01) Onyutha, Charles
Despite the advances in methods of statistical and mathematical modeling, there is considerable lack of focus on improving how to judge models’quality. Coefficient of determination (R2) is arguably the most widely applied ‘goodness-of-fit’ metric in modelling and prediction of environmental systems. However, known issues of R2 are that it: (i) can be low and high for an accurate and imperfect model, respectively; (ii) yields the same value when we regress observed on modelled series and vice versa; and (iii) does not quantify a model’s bias. A new model skill score E and revised R-squared (RRS) are presented to combine correlation, bias measure and capacity to capture variability. Differences between E and RRS lie in the forms of correlation and variability measure used for each metric. Acceptability of E and RRS was demonstrated through comparison of results from a large number of hydrological simulations. By applying E and RRS, the modeller can diagnostically identify and expose systematic issues behind model optimizations based on other ‘goodness-of-fits’ such as Nash–Sutcliffe efficiency (NSE) and mean squared error. Unlike NSE, which varies from ∞ to 1, E and RRS occur over the range 0–1. MATLAB codes for computing E and RRS are provided.
Hydrological model supported by a step-wise calibration against sub-flows and validation of extreme flow events
(MDPI, 2019-01-31) Onyutha, Charles
Most hydrological models have fixed structures and their calibrations are typified by a conventional approach in which the overall water balance closure is considered (without a step-wise focus on sub-flows’ variation). Eventually, hydrological modelers are confronted with the difficulty of ensuring both the observed high flows and low flows are accurately reproduced in a single calibration. This study introduced Hydrological Model focusing on Sub-flows’ Variation (HMSV). Calibration of HMSV follows a carefully designed framework comprising sub-flow’s separation, modeling of sub-flows, and checking validity of hydrological extremes. The introduced model and calibration framework were tested using hydro-meteorological data from the Blue Nile Basin of Ethiopia in Africa. When the conventional calibration approach was adopted through automatic optimization strategy, results from the HMSV were found highly comparable with those of five internationally well recognized hydrological models (AWBM, IHACRES, SACRAMENTO, SIMHYD, and TANK). The new framework enhanced the HMSV performance for reproducing quantiles of both high flows and low flows. The combination of flow separation and step-wise calibration of hydrological model against sub-flows enhances the modeler’s physical insight in identifying which areas need focus in modeling to obtain meaningful simulation results, especially of extreme events. The link for downloading the HMSV is provided
Impacts of climate variability and changing land use/land cover on River Mpanga flows in Uganda, East Africa
(Environmental Challenges : Elsevier, 2021-12-09) Onyutha, Charles; Turyahabwe, Catherine; Kaweesa, Paul
We analyzed River Mpanga Catchment (RMC) land use/land cover (LULC) types based on Landsat images for 2000, 2008 and 2014. Soil and Water Assessment Tool (SWAT) was driven by daily meteorological data from 2000 to 2011 to investigate impacts of LULC changes on river flow variation. In 2000, 2008, and 2014, cropland covered 33.0%, 69.1%, and 72.2% of RMC area, respectively. However, the fractions of the RMC area covered by grassland in 2000, 2008, and 2014 were 39.4%, 12.5%, and 10.4%, respectively. The portion of RMC area covered by human settlement increased from 0.2% in 2000 to 0.5% by 2014. RMC was characterized by increasing trends in annual rainfall and river flows. SWAT calibration and validation at daily scale over the periods 2000–2005 and 2006–2011 yielded Nash Sutcliffe Efficiency of 0.77 and 0.75, respectively. Contribution from transitions in LULC types to river flow changes over the period 2000–2008 was 7.65%. Generally, 70.46% of the total river flow variation was contributed by climate variability in terms of changes in climatic conditions. However, 21.89% of the total river flow variance remained unexplained and this could be attributed to other factors not considered in this study including extra impacts of human activities such water abstractions for agricultural, industrial and domestic needs. These findings are important for planning predictive land and water resources management amidst impacts of climate variability and human activities on water resources.
Investigating false start of the main growing season: a case of Uganda in East Africa
(Heliyon, 2021-11-19) Ocen, Emmanuel; de Bie, C.A.J.M.; Onyutha, Charles
False start of the growing season (Fsos) is a component of the onset variability related to agronomic drought that adversely impact on agricultural production and productivity. In the sub-Saharan Africa (SSA) where agriculture heavily depends on rainfall, the Fsos tends to create confusion among farmers on when to start planting crops thereby affecting seed germination and normal growth after emergence. In this paper, we focus on the Fsos and the occurrence of dry spell especially before the Start of growing Season (SoS). We take advantage of the existing rainfall estimates (CHIRPS) and remotely sensed data for vegetation performance (NDVI) over the period 1999–2017 in combination with local knowledge derived from farmers to map out areas at risk of (i) dry spell at the SoS, and (ii) false timing of SoS or high probability of occurrence of the Fsos. We found that the North Eastern part of Uganda (8.8% of arable area) were at risk of dry spell throughout each year. However, the greater North (58.1% of arable area) was prone to dry spell during the onset of the March–May season. Areas in the South Western (3.7%) region were at risk during the onset of the September–November season. The probability that a location in Uganda experiences an Fsos falls between 0-53%. The findings in this study are vital for planning of predictive adaptation to the impacts of climate variability on agriculture amid struggle aimed at tackling food insecurity challenge in the SSA.
Long-term climatic water availability trends and variability across the African continent
(Theoretical and Applied Climatology : Springer Link, 2021-06-29) Onyutha, Charles
This study analyzed trends and variability in climatic water availability (CWA) across the African continent using monthly precipitation and potential evapotranspiration (PET) over the period 1901–2015. Climatic water availability was characterized in terms of precipitation minus PET totals. Predictability of the variation in CWA was tested using climate indices. Large positive values of the CWA (or few drought incidents) were confined to areas (such as sub-region along the Gulf of Guinea, the western part of the equatorial region, and the Ethiopian Highlands) that receive large amounts of precipitation. Drought incidence in these areas was generally low and characterized by severity in the range 0–44% indicating moderate to extreme wetness. Areas which experienced increasing CWA or wetting trends were confined within the Tropics. These wetting trends were mostly insignificant (p > 0.05). Drying trends (or decreasing CWA) occurred mainly in areas outside the Tropics. These drying trends (especially in the CWA of the months from April to September) were mainly significant (p < 0.05) over the Sahara desert. CWA variability in the southern and eastern parts of Africa was negatively and positively correlated with Niño 3, respectively. Variability of the East African CWA was also positively correlated with the Indian Ocean Dipole (IOD). CWA variability in West Africa (or Sahel) was negatively correlated with Niño 3. Variability of West African CWA was also linked to changes in the sea surface temperature over the Atlantic Ocean. Based on multiple linear regression, predictability of variation in CWA using combinations of climate indices varied across regions and among time scales. For instance, using combination of IOD and Niño 3 as predictors, up to about 40% and less than 10% of the total variance in CWA across East Africa and area north of the Sahel belt could be explained, respectively.
Modelling chlorine residuals in drinking water: a review
(International Journal of Environmental Science and Technology, 2022-01-24) Onyutha, Charles; Kwio-Tamale, J. C.
World Health Organization’s guidelines on water quality limit concentrations of residual chlorine in drinking water to the range 0.2–5 mg/l. Modelling tends to be applied to understand how chlorine concentrations can be kept within the recommended limits. In this line, we reviewed 105 articles to show advances in modelling of chlorine residuals while focussing on both data-driven statistical models and process-based models. A total of 83 and 17% reviewed articles applied process-based models and statistical models, respectively. The most influential water parameters which were reported for chlorine decay were pH and temperature. For statistical models, modellers reported a wide range of sizes of training, testing, validation sub-samples, and number of neurons in the hidden layers of the network. Thus, the use of novel fitness function to concurrently seek for the most accurate and compact solution was recommended. Most studies applied coefficient of determination (despite its issues such as failure to quantify bias) to evaluate model performance. We recommended revised coefficient of determination and hydrological model skill score to be used as “goodness-of-fits” metrics since they can quantify model’s bias, and capacity to reproduce observed variability. We found that many modellers portrayed a common practice of not providing sufficient information (such as values of parameters) regarding their modelling results. For instance, 47% of the reviewed articles did not expressly specify the order of reaction in their chlorine decay modelling studies. The practice of not reporting sufficient pertinent information can affect reproducibility of results and hinder model improvement which would arise from possible follow-up studies.
Negative emotions about climate change are related to insomnia symptoms and mental health: cross-sectional evidence from 25 countries
(Current Psychology : Springer, 2021-02-16) Ogunbode, Charles Adedayo; Pallesen, Ståle; Böhm, Gisela; Doran, Rouven; Bhullar, Navjot; Aquino, Sibele; Marot, Tiago; Schermer, Julie Aitken; Wlodarczyk, Anna; Lu, Su; Jiang, Feng; Salmela-Aro, Katariina; Hanss, Daniel; Maran, Daniela Acquadro; Ardi, Rahkman; Chegeni, Razieh; Tahir, Hajra; Ghanbarian, Elahe; Park, Joonha; Tsubakita, Takashi; Tan, Chee-Seng; Broek, Karlijn L. van den; Chukwuorji, JohnBosco Chika; Ojewumi, Kehinde; Reyes, Marc Eric S.; Lins, Samuel; Enea, Violeta; Volkodav, Tatiana; Sollar, Omas; Navarro-Carrillo, Ginés; Torres-Marín, Jorge; Mbungu, Winfred; Onyutha, Charles; Lomas, Michael J.
Climate change threatens mental health via increasing exposure to the social and economic disruptions created by extreme weather and large-scale climatic events, as well as through the anxiety associated with recognising the existential threat posed by the climate crisis. Considering the growing levels of climate change awareness across the world, negative emotions like anxiety and worry about climate-related risks are a potentially pervasive conduit for the adverse impacts of climate change on mental health. In this study, we examined how negative climate-related emotions relate to sleep and mental health among a diverse non-representative sample of individuals recruited from 25 countries, as well as a Norwegian nationally-representative sample. Overall, we found that negative climate-related emotions are positively associated with insomnia symptoms and negatively related to self-rated mental health in most countries. Our findings suggest that climate-related psychological stressors are significantly linked with mental health in many countries and draw attention to the need for cross-disciplinary research aimed at achieving rigorous empirical assessments of the unique challenge posed to mental health by negative emotional responses to climate change.
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.
Space–time prediction of residual chlorine in a water distribution network using artificial intelligence and the EPANET hydraulic model
(Water Practice & Technology, 2024-09-10) Kwio-Tamale, Julius Caesar; Onyutha, Charles
Insufficient knowledge of physical models and difficulty in fitting statistical models impair the choice of models to regulate residual chlorine in water distribution. This paper compared the performance of physical and statistical models in predicting residual chlorine concentrations in drinking water distribution. Drinking water was sampled from the downstream 128 water points water pipeline. Online chlorine concentrations were determined at water draw-off points. EPANET, the physical model, was used because of its efficiency in tracking dissolved chemicals. Statistical models used were regression, decision tree, random forest and artificial neural network. In the whole distribution network, the artificial neural network performed at R2 of 94%, multi-linear regression (62%), random forest (55%), decision tree (41%), and EPANET (24%). However, EPANET yielded improved performance with R2 above 70% when separately applied to individual sub-distribution networks; hence, is recommended for secondary chlorination in small distribution networks. For modelling large distribution networks, statistical models, especially an artificial neural network, are recommended. However, such cases still need support from confirmatory systems of interpretable parametric or hydraulic models that can achieve good performance with R2 80%. Water utilities can use these results to deploy model(s) for managing residual chlorine within safe limits of residual chlorine concentration in water distribution practice.
Turbidity reduction efficacies of seed kernels of Mango (Mangifera indica) genotypes in Uganda
(Elsevier, 2023-10) Onyutha, Charles; Auma, Nancy
Alum and ferric salts as traditional chemical coagulants for turbidity removal in water and wastewater are expensive, and have known harmful effects. Thus, attempts to replace the chemical coagulants with safe and effective natural solutions are increasingly being made in terms of research studies to investigate the coagulation efficacies of various plants one of which is Mango (Mangifera indica). It is worth noting that M. indica has various genotypes of different origins across the world. In this study, eight (8) common M. indica genotypes in Uganda were identified, protein contents of their seed kernels determined, and coagulating efficacies investigated. Coagulation solution of each selected genotype was obtained by dissolving 5 g powder of the selected Mango seed kernel sample in 100 mL of distilled water. Next, 5 mL of this coagulant (or stock) solution was used to treat 200 mL of turbid water with turbidity ranging from 15 to 120 NTU. Using 0.01 M hydrochloric acid as an extraction solvent, protein contents of selected genotypes including Apple mango, Kate, Kent, Bire, Doodo red, Takataka, Kagoogwa, and Tommy Atkins were 38.02 %, 30.66 %, 15.94 %, 22.11 %, 21.50 %, 16.98 %, 16.36 %, and 17.87 %, respectively. Efficacies of coagulant from Apple mango, Kate, Kent, Bire, Doodo red, Takataka, Kagoogwa, and Tommy Atkins seed kernel samples were 92.2, 89.3, 66.0, 78.7, 76.9, 71.1, 68.9, and 73.1 %, respectively. Apple mango was the best performing genotype as a coagulant and this was followed by Kate. Coagulation efficacy was generally found to increase with increasing turbidity and/or coagulant’s concentration. For instance, Apple Mango coagulant removed 16.7 %, 50.3 %, and 92.2 % of initial turbidity 15, 65 and 120 NTU, respectively. Kent removed 57.5, 66, and 69 % of initial turbidity 120 NTU using 5, 20, and 30 mL of stock solution, respectively. This study demonstrated the influence of the choice of a plant genotype on coagulation efficacy.
Water availability trends across water management zonesin Uganda
(Atmospheric Science Letters, 2021-06-21) Onyutha, Charles; Asiimwe, Arnold; Muhwezi, Lawrence; Mubialiwo, Ambrose
This study assessed trends in gridded (0.25° × 0.25°) Climate Forecast System Reanalysis (CFSR) precipitation, potential evapotranspiration (PET), and precipitation minus PET (PMP) across the four water management zones (WMZs) in Uganda including Kyoga, Victoria, Albert, and Upper Nile. The period considered was 1979–2013. Validation of CFSR datasets was conducted using precipitation observed at eight meteorological stations across the country. Observed precipitation trend direction was satisfactorily reproduced by CFSR data extracted at five out of eight stations. Negative (positive) values of long-term PMP mean were considered to indicate areas characterized by water scarcity (surplus). Areas with large positive PMP were confined to Lake Victoria and mountains such as Rwenzori and Elgon. The largest negative PMP values were in the arid and semi-arid areas of north and northeastern Uganda. The null hypothesis H0 (no trend) was rejected (p < 0.05) for increasing annual precipitation trends across the various WMZs except in the extreme eastern parts of the Upper Nile, Kyoga, and Victoria WMZs (or areas along the boundary of Uganda and Kenya). The H0 (no trend) was rejected (p < 0.05) for decreasing trends in annual PET over West Nile region of the Upper Nile, western parts of Victoria, and the Albert WMZs. For increasing trend in PMP, the H0 (no trend) was rejected (p < 0.05) across the various WMZs except around the Mount Elgon area. The study findings are relevant for planning of water resources management across the different WMZs in the country.