Browsing by Author "Onyutha, Charles"

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    A hydrological model skill score and revised R-squared
    (Hydrology Research, 2022) Onyutha, Charles
    Despite the advances in methods of statistical andmathematical modeling, there is considerable lack of focus on improving how to judgemodels’ quality. Coefficient of determination (R2) is arguably the most widely applied ‘goodness-of-fit’ metric inmodelling 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 variabilitymeasure used for eachmetric. 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 othDespite the advances in methods of statistical andmathematical modeling, there is considerable lack of focus on improving how to judgemodels’ quality. Coefficient of determination (R2) is arguably the most widely applied ‘goodness-of-fit’ metric inmodelling 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 variabilitymeasure used for eachmetric. 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 oDespite the advances in methods of statistical andmathematical modeling, there is considerable lack of focus on improving how to judgemodels’ quality. Coefficient of determination (R2) is arguably the most widely applied ‘goodness-of-fit’ metric inmodelling 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 variabilitymeasure used for eachmetric. 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.ther ‘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.er ‘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.
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    African crop production trends are insufficient to guarantee food security in the sub-Saharan region by 2050 owing to persistent poverty
    (Food Security, 2018) Onyutha, Charles
    To meet the future food demand, supply should be increased. Crop production in Africa is projected to increase in the future. However, can the crop production trends guarantee future food security? For illustrative analyses, cereal was used on the assumption, following a recent study, that the changes in its production are representative of those for other major food crops. For 50 African countries, trends and variability in cereal production, yield, and area harvested from 1961 to 2014 as well as the ratio of production to population (RPP) were analyzed by testing the null hypothesis H0 (no trend) and H0 (natural randomness) at α = 0.05. For negative (positive) trends in production, yield, area harvested, and RPP, respectively, H0 (no trend) was rejected (p < 0.05) in 2% (63%), 0% (38%), 2% (45%) and 15%(4%) of the countries. Regardless of the trend significance, there was an increase (a decrease) in production and RPP of 94% (6%) and 29% (71%), respectively, of the countries. Cereal production, yield, and area harvested as well as RPP exhibited positive and negative anomalies in a clustered way in time. In 78%of the countries, whereas cereal production exhibited a positive trend, RPP was characterized by a decrease. The H0 (natural randomness) was rejected (p< 0.05) for negative anomalies in RPP of many 75% of the countries. In 87% of the African countries, cereal production was significantly (p < 0.05) linked to area harvested. The characterization of RPP by both an oscillatory behavior over multi-decadal time scales and a general negative trend suggests that the possible optimism in the projected increase in food production should be taken prudently. By 2050, poverty will still be at significant levels thereby strongly causing food insecurity in many of the African countries (especially from the sub-Saharan region). To ensure food security, it is recommended that yield gap closure should be supplemented with an improvement of access to markets for smallholder farmers, and promotion of income generating activities outside farming. Furthermore, disparity in initiatives of regional and national scales should be addressed, and the differences in priorities across various sub-sectors of farming in each country and Africa as a whole must be minimized.
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    Analyses of community willingness-to-pay and the influencing factors towards restoration of River Malaba floodplains
    (Environmental Challenges, 2021) Mubialiwo, Ambrose; Abebe, Adane; Onyutha, Charles
    The high productivity of soils along River Malaba floodplains and various functions (like, transport and recreation) increase the desire for humankind settlement adjacent to floodplain corridors. However, human life and property have unceasingly been destroyed by floods. Strategies have been established to deal with floods but the problem still exists. This study employed the double-bound dichotomous choice contingent valuation method to quantify the community willingness-to-pay (WTP) and associated influencing factors for restoration of River Malaba floodplains. Reconnaissance surveys, focus group discussions, key informant interviews, observations study, and household questionnaires from 498 out of the targeted 550 respondents were employed in data collection. Among the adaptation strategies at household and community level, the post-flood strategies were more efficient than those practiced before- and during-floods. Among the suggested structural and non-structural strategies, “embankment/river training structures” and “flood forecasting and early warning” were highly preferred, respectively. The results revealed that 55% of the households expressed WTP an individual amount between Uganda shillings (UGX) 5,000 (United States Dollar, US$ 1.35) to UGX 500,000 (US$ 135.14), with a monthly average of UGX 97,080 (US$ 26.24). Total monthly amount would be UGX 38,249,500 (US$ 10,333.70) considering the 498 households. Among the factors analysed, age, gender, marital status, education level, occupation, household income, business affected, lost property due to floods, flooding a major problem had significant (p<0.01) positive impact on WTP. This study findings are pertinent in supporting stakeholders’ decision regarding predictive planning of flood adaptation strategies in the study area.
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    Analyses of Precipitation and Evapotranspiration Changes across the Lake Kyoga Basin in East Africa
    (Water, 2020) 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.
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    Analyses of rainfall extremes in East Africa based on observations from rain gauges and climate change simulations by CORDEX RCMs
    (Climate Dynamics, 2020) Onyutha, Charles
    This study derived twelve Extreme Rainfall Indices (ERIs) such as the Maximum Dry Spell (MDS) and Maximum Wet Spell (MWS) from daily rainfall observed over the period 1961–1990 at nine locations across East Africa. Capacity of six CO-ordinated Regional Climate Downscaling EXperiment (CORDEX) Africa Regional Climate Models (RCMs) driven by twenty six Climate Model Intercomparison Project phase 5 (CMIP5) General Circulation Models (GCMs) to reproduce the observed ERIs with respect to long-term mean and trends was evaluated. Four RCMs and their five driving GCMs were further analyzed with respect to ERIs. Ensemble means of the RCMs’ biases in simulating trends in several ERIs were of magnitudes above 50%. On average, biases in reproducing long-term mean were smaller than those for trends in ERIs. The difference between the performances of RCMs and GCMs depended on the selected RCM–GCM pair. The ensemble means of the RCMs reproduced observed ERIs better than the individual RCMs corroborating that the use of multi-model ensembles can boost credibility of climate change simulations and projections. The RCMs performed better than their driving GCMs in reproducing MDS. The biases of both the RCMs and GCMs were smaller in reproducing the MWS than MDS. Nonetheless, in reproducing observed MWS, the ensemble mean of RCMs’ biases was slightly larger than that of the driving GCMs indicating possible adding up of the uncertainties from the GCMs and RCMs. Suggested RCMs’ improvements regarding aerosol impacts on rainfall include adding missing constituents (like nitrate), and refining the crudely represented components. RCMs also require high resolution description (in both space and time) of land use types, land surface covers and characteristics as well as landscape heterogeneity. The GCMs to be used as the initial and lateral boundary conditions for the RCMs require improvement in their representation of key dynamical and thermodynamical feedbacks in the Tropical Indian Ocean.
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    Analyses of rainfall trends in the Nile River Basin
    (Journal of hydro-environment research, 2016) Onyutha, Charles; Tabari, Hossein; Taye, Meron T.; Nyandwaro, Gilbert N.; Willems, Patrick
    Trends in rainfall at 39 locations of the Nile River Basin (NRB) in Africa were analyzed. Comparison was made between rainfall trend results from the long-term data and those of short-term series selected over different time periods. The bias on trend results from series of short-term records was quantified. Homogeneity test was conducted to assess the coherence of the trend directions on a regional basis. Based on an assumed population (for simplicity) of rainfall data time periods in the range 75–100 years, bias in the short-term trend analysis was noted to reduce by about 10% for every 10% increase in record length. Under some conditions if respected, it was possible to derive trends at stations with short records based on those at nearby stations with longer term records but in the same region. Using the same data record length and uniform time period at all the selected stations, an improved regional coherence of rainfall trend results was obtained. In the equatorial region, trend in annual rainfall was found mainly positive and significant at level α = 5% in 4 of the 7 stations. Collectively for Sudan, Ethiopia and Egypt, trends in the annual rainfall were mostly negative and significant at α = 5% in 69% of the 32 stations. Heterogeneity in the trend directions for the entire NRB was confirmed at α = 1% in 13% of the 39 stations. These findings are vital for water and agricultural management practices. © 2015 International Association for Hydro-environment Engineering and Research, Asia Pacific Division. Published by Elsevier B.V. All rights reserved.
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    Assessment of the Effects of Procurement Planning Processes on Performance of Construction Contracts in Local Governments in Uganda
    (Journal of Civil, Construction and Environmental Engineering, 2020) Muhwezi, Lawrence; Tumusime Musiime, Fred; Onyutha, Charles
    Procurement as one of the core functions of public sector agencies in Uganda has become a big matter of concern as expenditure on its processes have continued to be alarmingly high in government departments including local governments. This study assessed the effects of procurement processes on performance of construction contracts in Local Governments in Uganda. The study adopted a descriptive research design and used purposive sampling to sample 81 respondents. Data were collected using questionnaire and analyzed using SPSS software. The study revealed that poor procurement planning led to big budget deficits with a mean of 1.86 thus affecting performance of construction contracts to a very large extent. The study further revealed that funds for the construction contracts are not always released in time and this has a big effect on performance of construction contracts. The study concluded that procurement planning and contract monitoring and administration have a significant effect on the performance of construction contracts in District Local Governments in Uganda. The study recommended that Local Governments should adopt the developed model to control the procurement process and other anomalies in the award and management of construction contracts.
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    Changes in precipitation and evapotranspiration over Lokok and Lokere catchments in Uganda
    (Bulletin of Atmospheric Science and Technology, 2021) 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.
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    Combined Use of Graphical and Statistical Approaches for Analyzing Historical Precipitation Changes in the Black Sea Region of Turkey
    (Water, 2020) Mustafa Cengiz, Taner; 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 di erence 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.
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    Contribution of climatic variability and human activities to stream flow changes in the Haraz River basin, northern Iran
    (Journal of Hydro-Environment Research, 2019) Pirniaa, Abdollah; Choubin, Bahram; Omidvar, Ebrahim; Onyutha, Charles; Torabi Haghighi, Ali
    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.
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    Contributions of Human Activities and Climatic Variability to Changes in River Rwizi Flows in Uganda, East Africa
    (Hydrology, 2021) Onyutha, Charles; Nyesigire, Resty; Nakagiri, Anne
    This study employed Soil and Water Assessment Tool (SWAT) to analyze the impacts of climate variability and human activities on River Rwizi flows. Changes in land use and land cover (LULC) types from 1997 to 2019 were characterized using remotely sensed images retrieved from Landsat ETM/TM satellites. SWAT was calibrated and validated over the periods 2002–2008 and 2009–2013, respectively. Correlation between rainfall and river flow was analyzed. By keeping the optimal values of model parameters fixed while varying the LULC maps, differences in the modeled flows were taken to reflect the impacts of LULC changes on rainfall–runoff generation. Impacts due to human activities included contributions from changes in LULC types and the rates of water abstracted from the river as a percentage of the observed flow. Climate variability was considered in terms of changes in climatic variables such as rainfall and evapotranspiration, among others. Variability of rainfall was analyzed with respect to changes in large-scale ocean-atmosphere conditions. From 2000 to 2014, the portion of River Rwizi catchment area covered by cropland increased from 23.0% to 51.6%, grassland reduced from 63.3% to 37.8%, and wetland decreased from 8.1% to 4.7%. Nash–Sutcliffe Efficiency values for calibration and validation were 0.60 and 0.71, respectively. Contributions of human activities to monthly river flow changes varied from 2.3% to 23.5%. Impacts of human activities on the river flow were on average found to be larger during the dry (14.7%) than wet (5.8%) season. Using rainfall, 20.9% of the total river flow variance was explained. However, climate variability contributed 73% of the river flow changes. Rainfall was positively and negatively correlated with Indian Ocean Dipole (IOD) and Niño 3, respectively. The largest percentages of the total rainfall variance explained by IOD and Niño 3 were 12.7% and 9.8%, respectively. The magnitude of the correlation between rainfall and IOD decreased with increasing lag in time. These findings are relevant for developing River Rwizi catchment management plans.
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    Decadal Analysis of River Flow Extremes Using Quantile-Based Approaches
    (er Resources Management, 2017) Tabari, Hossein; Teferi Taye, Meron; Onyutha, Charles; Willems, Patrick
    Next to the traditional analysis of trends in time series of hydro-climatological variables, analysis of decadal oscillations in these variables is of particular importance for the risk assessment of hydro-climatological disasters and risk-based decision-making. Conventional parametric and nonparametric tests, however, need implementing a set of background assumptions related to serial structure and statistical distribution of data. They neither focus on the extreme events and their probability of occurrence. In order to get rid of these limitations, we suggest a modified version of the Sen Method (SM), combined with the Quantile Perturbation Method (QPM) for examining temporal variation of extreme hydrological events. The developed method is tested for decadal analysis of monthly and annual river flows at 10 hydrometric stations in the Qazvin plain in Iran. The results show oscillatory patterns in extreme river flow quantiles, with a positive anomaly during the 1990s and a negative one during the 2000s. It is also shown that the concurrent use of the two methods allows to set a complete picture on the temporal changes in high and low extremes in historical river flow observations in different seasons.
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    Empirical statistical characterization and regionalization of amplitude–duration–frequency curves for extreme peak flows in the Lake Victoria Basin, East Africa
    (Hydrological Sciences Journal, 2015) Onyutha, Charles; Willems, Patrick
    This paper focuses on a regionalization attempt to partly solve data limitation problems in statistical analysis of high flows to derive discharge–duration–frequency (QDF) relationships. The analysis is based on 24 selected catchments in the Lake Victoria Basin (LVB) in East Africa. Characteristics of the theoretical QDF relationships were parameterized to capture their slopes of extreme value distributions (evd), tail behaviour and scaling measures. To enable QDF estimates to be obtained for ungauged catchments, interdependence relationships between the QDF parameters were identified, and regional regression models were developed to explain the regional difference in these parameters from physiographic characteristics. In validation of the regression models, from the lowest (5 years) to the highest (25 years) return periods considered, the percentage bias in the QDF estimates ranged from –2% for the 5-year return period to 27% for 25-year return period.
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    From R-squared to coefficient of model accuracy for assessing "goodness-of-fits"
    (Geoscientific Model Development Discussions, 2020) Onyutha, Charles
    Modelers tend to focus more on advancing methods of statistical and mathematical modeling than developing novel techniques for comparing modeled results with observations or establishing metrics for model performance assessment. Perhaps solely the most extensively applied "goodness-of-fit" measure especially for assessing performance of regression models is the coefficient of determination R2. Normally, high R2 tends to be associated with an efficient model. Nevertheless, R2 has been cited to have no importance in the classical model of regression. Even in its use in descriptive statistics, R2 is known to have questionable justification. R2 is inadequate in assessing model performance because it does not give any information on the model residuals. Furthermore, R2 can be low for an effective model. Contrastingly, a very poor model fit can yield high R2. Regressing X on Y yields R2 which is the same as that if Y is regressed on X thereby invalidating its use as a coefficient of determination. Taking into account the drawbacks of using R2, this paper introduces coefficient of model accuracy (CMA) the derivation of which comprises an analogy to the R2. However, instead of simply squaring an ordinary Pearson's product-moment correlation coefficient to obtain R2, CMA comprises the product of nonparametric sample correlation and model bias. Acceptability of the introduced method can be found demonstrated through comparison of results from simulations by hydrological models calibrated using CMA and other existing objective functions. MATLAB and R codes as well as an illustrative MS Excel file to compute the CMA were provided.
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    Geospatial Trends and Decadal Anomalies in Extreme Rainfall over Uganda, East Africa
    (Onyutha, C. (2016). Geospatial trends and decadal anomalies in extreme rainfall over Uganda, East Africa. Advances in Meteorology, 2016. http://dx.doi.org/10.1155/2016/6935912, 2016) Onyutha, Charles
    Trends and variability in series comprising the mean of fifteen highest daily rainfall intensities in each year were analyzed considering entire Uganda.The data were extracted from high-resolution (0.5∘ × 0.5∘) gridded daily series of the Princeton Global Forcings covering the period 1948–2008. Variability was analyzed using nonparametric anomaly indicator method and empirical orthogonal functions. Possible drivers of the rainfall variability were investigated. Trends were analyzed using the cumulative rank difference approach. Generally, rainfall was above the long-term mean from the mid-1950s to the late 1960s and again in the 1990s. From around 1970 to the late 1980s, rainfall was characterized by a decrease. The first and second dominant modes of variability correspond with the variation in Indian Ocean Dipole and North Atlantic Ocean index, respectively.The influence of Ni˜no 3 on the rainfall variability of some parts of the country was also evident.The southern and northern parts had positive and negative trends, respectively.The null hypothesis 𝐻0 (no trend) was collectively rejected at the significance level of 5% in the series from 7 out of 168 grid points. The insights from the findings of this study are vital for planning and management of risk-based water resources applications.
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    Graphical-statistical method to explore variability of hydrological time series
    (Hydrology Research, 2021) 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.
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    Historical Rainfall and Evapotranspiration Changes over Mpologoma Catchment in Uganda
    (Advances in Meteorology, 2020) Mubialiwo, Ambrose; Onyutha, Charles; Abebe, Adane
    Changes in the long-term (1948–2016) rainfall and evapotranspiration over Mpologoma catchment were analysed using gridded (0.25° × 0.25°) Princeton Global Forcing data. Trend and variability were assessed using a nonparametric approach based on the cumulative sum of the difference between exceedance and nonexceedance counts of data. Annual and March-May (MAM) rainfall displayed a positive trend (p < 0.05), whereas October-December (OND) and June-September rainfall exhibited negative trends with p > 0.05 and p < 0.05, respectively. Positive subtrends in rainfall occurred in the 1950s and from the mid-2000s till 2016; however, negative subtrends existed between 1960 till around 2005. Seasonal evapotranspiration exhibited a positive trend (p > 0.05). For the entire period (1948–2016), there was no negative subtrend in the OND and MAM evapotranspiration. Rainfall and evapotranspiration trends and oscillatory variation in subtrends over multidecadal time scales indicate the need for careful planning of predictive adaptation to the impacts of climate variability on environmental applications which depend on water balance in the Mpologoma catchment. It is recommended that future studies quantify possible contributions of human factors on the variability of rainfall and evapotranspiration. Furthermore, climate change impacts on rainfall and evapotranspiration across the study area should be investigated.
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    How well do climate models reproduce variability in observed rainfall? A case study of the Lake Victoria basin considering CMIP3, CMIP5 and CORDEX simulations
    (Stochastic Environmental Research and Risk Assessment, 2019) Onyutha, Charles; Rutkowska, Agnieszka; Nyeko-Ogiramoi, Paul; Willems, Patrick
    In this study, how well the climate models reproduce variability in observed rainfall was assessed based on General Circulation Models (GCMs) from phase 3 and phase 5 of the Coupled Model Inter-comparison Project, i.e., CMIP3 and CMIP5, respectively as well as the Regional Climate Models (RCMs) of COordinated Regional Climate Downscaling EXperiment (CORDEX) over Africa. Observed and climate model based daily rainfall across the Lake Victoria Basin, which is one of the wettest parts of Africa, was considered. Temporal variability was assessed based on the coefficient of variation of daily and annual rainfall, and the maximum dry and wet spell in each year. Furthermore, variation in daily rainfall was assessed in terms of the long-range dependence. Comparison of variability results from observed and climate model based rainfall was made. It was found that the capacity to reproduce variability in observed wet and dry conditions depends on the specific GCM (of CMIP3 or CMIP5) or CORDEX RCM. However, the CORDEX RCMs replicated variability in observed daily rainfall better than the CMIP3 and CMIP5 GCMs. This was due to the spatial resolutions of the CORDEX RCMs which are higher than those of the CMIP3 and CMIP5 GCMs. The ensemble mean of the coefficients of correlation between the variability in observed and that of climate model based rainfall was close to zero for both the GCMs or RCMs. This suggests that analyses can be done on a case by case basis. In other words, GCMs or RCMs which adequately reproduce variability in observed wet and dry conditions can be considered for further statistical analysis of the changes especially on the basis of statistical methods for downscaling. For daily timescale, both the GCMs and RCMs from all the three sets of climate models generally exhibited poor performance in capturing the time of occurrences and the magnitudes of rainfall events (when considered in a combined way). To reliably assess long-term rainfall changes, it is vital to characterize natural variation in terms of the statistical dependence. With respect to natural variability of rainfall at local scales, there is room for further improvement of the climate models; however, whether theory of fractals and/or concepts of scaling behavior or self-similarity can explicitly contribute in that respect is a crucial consideration. Results from this study gave some insights in the reasonableness of the future rainfall projections.
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    Hydrodynamic Modelling of Floods and Estimating Socio‑economic Impacts of Floods in Ugandan River Malaba Sub‑catchment
    (Earth Systems and Environment, 2022) Mubialiwo, Ambrose; Abebe, Adane; Serre Kawo, Nafyad; 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.
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    Hydrological Model Supported by a Step-Wise Calibration against Sub-Flows and Validation of Extreme Flow Events
    (Water, 2019) 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.