NRU :: Browsing by Author "Mugume, Isaac"

Browsing by Author "Mugume, Isaac"

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An Assessment of the Effectiveness ofWeather Information Dissemination among Farmers and Policy Makers
(Sustainability, 2022) Sansa-Otim, Julianne; Nsabagwa, Mary; Mwesigwa, Andrew; Faith, Becky; Owoseni, Mojisola; Osuolale, Olayinka; Mboma, Daudi; Khemis, Ben; Albino, Peter; Owusu Ansah, Samuel; Abla Ahiataku, Maureen; Owusu-Tawia, Victoria; Bashiru, Yahaya; Mugume, Isaac; Akol, Roseline; Kunya, Nathern; Inguula Odongo, Ronald
The changing environment, climate, and the increasing manifestation of disasters, has generated an increased demand for accurate and timely weather information. This information is provided by the National meteorological authorities (NMAs) through different dissemination channels e.g., using radios, Televisions, emails among others. The use of ICTs to provide weather information is recently gaining popularity. A study was conducted in three countries, namely Nigeria, Uganda, and South Sudan to assess the efficiency of an ICT tool, known as “Weather Information Dissemination System”. The study involved 254 participants (Uganda: 71; South Sudan: 133; and Nigeria: 50). The collected primary data were first quality controlled and organized thematically for detailed analysis. Descriptive statistics was used to provide quantitative analysis as well as content scrutinized for qualitative analysis. The results showed that there is a need for timely weather information to plan farming activities such as planting and application of fertilizers and pesticides as well as to manage flood and drought by the water sector and disaster management. Results further showed that the majority of the respondents have access to the technology needed to access weather and climate information. The respondents who received weather information from NMAs noted that the forecast was good. However, they further noted that there is more room for improvement especially with making the forecasts location-specific, ensuring mobile access is adequate in all regions, provision of weather information by SMS (in countries where this service is currently unavailable) and improved timing of the weather information. Finally, uncertainty about the accuracy of weather information and the weather information not meeting specific needs are key barriers to people’s willingness to pay for it (Uganda: 33.3%; South Sudan: 46.1%; and Nigeria: 33.3%). Improved collaborations between the NMAs, ICT service providers, policymakers and farmers will facilitate an effective approach to weather information access and dissemination. Innovative sensitization approaches through the media houses will enable better understanding of weather products and utilization, and access to enabling ICTs would increase access to weather forecasts
Community views on water demands under a changing climate: The case of River Mpanga Water Catchment, Western Uganda
(African Journal of Environmental Science and Technology, 2021) Mugume, Isaac; Semyalo, Ronald; Wasswa, Peter; Ngailo, Triphonia; Inguula Odongo, Ronald; Lunyolo, Joyce; Tao, Sulin
Different sectors globally are experiencing the impacts of changing climate and water resources are among them. This study was conducted with an aim of examining the community views regarding the effect of changing climate on water demand over the River Mpanga Water Catchment. The study employed a cross-sectional survey using 111 household interviews; 14 Focus Group Discussions (FGDs) and 27 key informants interviews (KII). This study considered 14 villages and employed a mixed-methods study design. The analysis was conducted using SPSS software to derive the descriptive statistics. Qualitative information was analyzed using content analysis to conduct an in-depth analysis. The study found that the main source of water is tap water (72.1%) and the main use of water in the study area is domestic water use. This study also found that, breakage in water supply especially during the dry season (10 out of 14 FGDs) and poor quality of water especially the tap water due to chemical treatment (11 out of 14 FDGs) were the major challenges of water the community faced. Additionally, this study observed that 15 out of 27 KII considered drought as a major threat and that the area had experienced decreases in rainfall amounts over the months of January and February. Therefore, this study recommends that the providers of domestic water should invest heavily in technologies for improving water quality and amount; ensure sustainable and equitable rationing of water during scarcity; and promote incentives for water harvesting.
The damage caused by landslides in socio-economic spheres within the Kigezi highlands of South Western Uganda
(Environmental & Socio-economic Studies, 2021) Nseka, Denis; Mugagga, Frank; Opedes, Hosea; Ayesiga, Patience; Wasswa, Hannington; Mugume, Isaac; Nimusiima, Alex; Nalwanga, Faridah
An assessment of the socio-economic implications of landslide occurrence in the Kigezi highlands of South Western Uganda was conducted. Landslide occurrence is on the increase and threatens community livelihoods in these highlands. Detailed field investigations were undertaken with the help of local communities between June 2018 and May 2020 to identify and map recent and visible landslide scars in Rukiga uplands of Kigezi highlands. In the course of field inventories, 85 visible landslide scars were identified and mapped using handheld GPS receivers to produce a landslide distribution map for the study area. A socio-economic analysis was conducted to establish the effects of landslide damage on people’s livelihoods as well as their existing coping and adaptation mechanisms. The assessment was administered through field observations and surveying, focus group discussions, key informants and household interviews as well as the use of Local Government Environmental Reports. The study established an increase in the spatial-temporal distribution of landslides over the Kigezi highlands in the past 40 years. The landslides have resulted in a reduction in the quality of land, loss of lives, destruction of transport infrastructures, settlements, farmlands, crops and other socio-economic infrastructures. Therefore, it is important to look for reliable and sustainable measures to prevent landslide hazards. Total landscape reforestation with deep-rooted trees can possibly reduce the landslide risk. It is also important to undertake policy implementation for preparedness and mitigation plans against landslides in this region and in the country at large. Proper soil and water conservation measures could help in enhancing soil strength against landslide hazards.
Evaluation of WRF‑chem simulations of NO2 and CO from biomass burning over East Africa and its surrounding regions
(Atmospheric and Oceanic Sciences, 2022) Opio, Ronald; Mugume, Isaac; Nakatumba‑Nabende, Joyce; Nanteza, Jamiat; Nimusiima, Alex; Mbogga, Michael; Mugagga, Frank
In East Africa, biomass burning in the savanna region emits nitrogen dioxide ( NO2), carbon monoxide (CO), and aerosols among other species. These emissions are dangerous air pollutants which pose a health risk to the population. They also affect the radiation budget. Currently, limited academic research has been done to study their spatial and temporal distribution over this region by means of numerical modeling. This study therefore used the Weather Research and Forecasting model coupled with chemistry (WRF-chem) to simulate, for the first time, the distribution of NO2 during the year 2012 and CO during the period June 2015 to May 2016 over this region. These periods had the highest atmospheric abundances of these species. The model’s performance was evaluated against satellite observations from the Ozone Monitoring Instrument (OMI) and the Measurement of Pollution in the Troposphere (MOPITT). Three evaluation metrics were used, these were, the normalized mean bias (NMB), the root mean square error (RMSE) and Pearson’s correlation coefficient (R). Further, an attempt was made to reduce the bias shown by WRF-chem by applying a deep convolutional autoencoder (WRF-DCA) algorithm and linear scaling (WRF-LS). The results showed that WRF-chem simulated the seasonality of the gases but made below adequate estimates of the gas abundances. It overestimated NO2 and underestimated CO throughout all the seasons. Overall, for NO2, WRF-chem had an average NMB of 3.51, RMSE of 2 × 1015 molecules/cm2 and R of 0.44 while for CO, it had an average NMB of − 0.063, RMSE of 0.65 × 1018 molecules/cm2 and R of 0.13. Furthermore, even though both WRF-DCA and WRF-LS successfully reduced the bias in WRF-chem’s NO2 estimates, WRF-DCA had a superior performance compared to WRF-LS. It reduced the NMB by an average of 3.2 (90.2%). Finally, this study has shown that deep learning has a strong ability to improve the estimates of numerical models, and this can be a cue to incorporate this approach along other stages of the numerical modeling process.
Examining the Impact of Bias Correction on the Prediction Skill of Regional Climate Projections
(Atmospheric and Climate Sciences, 2020) Mugume, Isaac; Ngailo, Triphonia; Semyalo, Ronald
Rainfall is crucial for many applications e.g. agriculture, health, water resources, energy among many others. However, quantitative rainfall estimation is normally a challenge especially in areas with sparse rain gauge network. This has introduced uncertainties in rainfall projections by climate models. This study evaluates the performance of three representative concentration pathways, RCP i.e. 4.5, 6.0 and 8.5 over Uganda using the Weather Research and Forecasting (WRF) model. It evaluates the model output using observed daily rain gauge data over the period 2006-2018 using Pearson correlation; relative root mean square error; relative mean error and skill scores (accuracy). It also evaluates the potential improvement in the performance of the WRF model with respective RCPs by applying bias correction. The bias correction is carried out using the quantile mapping method. A poor correlation with observed rainfall is generally found (−0.4 to +0.4); error magnitudes in the ranges of 1 to 3.5 times the long-term mean are observed. The RCPs presented different performances over different areas suggesting that no one RCP is universally valid. Application of bias correction did not produce realistic improvement in performance. Largely, the RCPs underestimated rainfall over the study area suggesting that the projected rainfall cases under these RCPs could be seriously underestimated. However, the study found RCP8.5 with slightly better performance and is thus recommended. Due to the general weak performance of the RCPs, the study recommends re-evaluating the assumptions under the RCPs for different regions or attempt to improve them using data assimilation.
Micro-level analysis of climate-smart agriculture adoption and effect on household food security in semi-arid Nakasongola District in Uganda
(Environmental Research: Climate, 2022) Egeru, Anthony; Mwesiga Bbosa, Martha; Siya, Aggrey; Asiimwe, Robert; Mugume, Isaac
Climate-smart agriculture (CSA) is fronted as a sustainable, transformative, and technologically innovative approach that increases agricultural productivity, income and enhances greenhouse gas mitigation. However, there is limited micro-level evidence on the effects of the adoption of CSA on food security despite intensified promotion efforts in Uganda. A cross-sectional household survey among 165 respondents, undertaken in August–September 2020, was used to collect requisite data. Principal component analysis (PCA) with iteration and varimax rotation and analysis of variance were used in characterizing CSA practices. An ordered logit model was applied to identify the reported levels of CSA utility. Meanwhile, an endogenous switching regression was adopted to determine the effect of CSA adoption on household food security. Results showed that households used a combination of practices, including soil and water management, pasture management, livestock productivity and disease management. The PCA results revealed six major categories for the 16 most commonly used CSA practice combinations. The key factors that influenced the adoption of CSA practices among households included; access to climate information, total livestock units, ownership of non-livestock assets, and participation in off-farm activities. Results also revealed that the expected food consumption scores (FCS) for adopters and non-adopters were 53.87 and 66.92 respectively. However, when adopters and non-adopters were compared, we found that the adopters of CSA practices would have had a significantly lower counterfactual FCS had they not adopted CSA. While the adoption levels of CSA in this study is low, the counterfactual effects have shown that households that adopted CSA would have had a lower FCS and therefore lower food security status had they not adopted CSA. We recommend CSA promotional efforts that give more attention to combined CSA practices and respond to local production constraints.
Modeling the atmospheric dispersion of SO2 from Mount Nyiragongo
(Journal of African Earth Sciences, 2023) Opio, Ronald; Mugume, Isaac; Nakatumba-Nabende, Joyce; Mbogga, Michael
Mount Nyiragongo, an active volcano, is the most dominant natural source of sulphur dioxide (SO2) in Africa. While a number of studies have employed atmospheric models to simulate the dispersion of SO2 from this mountain, prior to this study, no attempt has been made to use deep learning to bias correct the model’s estimates. Here, the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) was used to simulate massive SO2 plumes degassed from this mountain between September 2014 and August 2015. Satellite observations by the Ozone Monitoring Instrument (OMI) showed that the SO2 spread to over 500 km from the volcano site. A deep convolutional autoencoder algorithm (WRF-DCA) was then applied to reduce the bias that WRF-Chem showed against the OMI observations. Finally, the correction performance of WRF-DCA was compared with a conventional bias correction method, linear scaling (WRF-LS). The performance of WRF-Chem, WRF-DCA, and WRF-LS was analyzed using three metrics, that is, the normalized mean bias (NMB), the root mean square error (RMSE), and Pearson’s correlation coefficient (R). The results showed that WRF-Chem overestimated SO2 at locations near the volcano site and underestimated SO2 at locations further away from the volcano site. It generated an overall average NMB of 􀀀 0.61 against the OMI observations. Respectively, WRFDCA and WRF-LS reduced this bias by an average of 0.25 (40.9%) and 0.21 (34.4%). Furthermore, although both methods also reduced the RMSE and improved the correlation, WRF-DCA consistently performed better than WRF-LS. This study demonstrates the advantage that deep learning can provide in estimating volcanic SO2 emissions.
Understanding the Trend of NO2, SO2 and CO over East Africa from 2005 to 2020
(Atmosphere, 2021) Opio, Ronald; Mugume, Isaac; Nakatumba-Nabende, Joyce
The atmospheric chemistry constituents of nitrogen dioxide (NO2), sulphur dioxide (SO2) and carbon monoxide (CO) are associated with air pollution and climate change. In sub-Saharan Africa, a lack of sufficient ground-based and aircraft observations has, for a long time, limited the study of these species. This study thus utilized satellite observations as an alternative source of data to study the abundance of these species over the East African region. The instruments used included the Ozone Monitoring Instrument (OMI), the Atmospheric InfraRed Sounder (AIRS), and the TROPOspheric Monitoring Instrument (TROPOMI). An investigation of trends in the data series from 2005 to 2020 was carried out using the sequential Mann-Kendall test while the Pearson correlation coefficient was used to compare the data records of the instruments. The analysis revealed no trend in NO2 (p > 0.05), a decreasing trend in SO2 (p < 0.05), a decreasing trend (p < 0.05) in CO closer to the surface (850 hPa to 500 hPa) and an increasing trend (p < 0.05) in CO higher up in the atmosphere (400 hPa to 1 hPa). There is likely a vertical ascent of CO. The correlation between the instrument records was 0.54 and 0.77 for NO2 and CO, respectively. Furthermore, seasonal fires in the savanna woodlands were identified as the major source of NO2 and CO over the region, while cities such as Kampala, Nairobi, and Bujumbura and towns such as Dar es Salaam and Mombasa were identified as important NO2 hotspots. Similarly, the active volcano at Mt. Nyiragongo near Goma was identified as the most important SO2 hotspot.
WRF Simulations of Extreme Rainfall over Uganda’s Lake Victoria Basin: Sensitivity to Parameterization, Model Resolution and Domain Size
(Journal of Geoscience and Environment Protection, 2020) Opio, Ronald; Sabiiti, Geoffrey; Nimusiima, Alex; Mugume, Isaac; Sansa-Otim, Julianne
Rainfall extremes have strong connotations to socio-economic activities and human well-being in Uganda’s Lake Victoria Basin (LVB). Reliable prediction and dissemination of extreme rainfall events are therefore of paramount im-portance to the region’s development agenda. The main objective of this study was to contribute to the prediction of rainfall extremes over this region using a numerical modelling approach. The Weather Research and Forecasting (WRF) model was used to simulate a 20-day period of extremely heavy rainfall that was observed in the March to May season of 2008. The underlying interest was to investigate the performance of different combinations of cumulus and mi-crophysical parameterization along with the model grid resolution and do-main size. The model output was validated against rainfall observations from the Tropical Rainfall Measuring Mission (TRMM) using 5 metrics; the rain-fall distribution, root mean square error, mean error, probability of detection and false alarm ratio. The results showed that the model was able to simulate extreme rainfall and the most satisfactory skill was obtained with a model se-tup using the Grell 3D cumulus scheme combined with the SBU_YLin micro-physical scheme. This study concludes that the WRF model can be used for simulating extreme rainfall over western LVB. In the other 2 regions, central and eastern LVB, its performance is limited by failure to simulate nocturnal rainfall. Furthermore, increasing the model grid resolution showed good po-tential for improving the model simulation especially when a large domain is used.

Browsing by Author "Mugume, Isaac"

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