Browsing by Author "Nanteza, Jamiat"

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    Empirical Relationships between Banana Yields and Climate Variability over Uganda
    (J. Environ. Agric. Sci, 2016) Sabiiti, Geoffrey; Ininda, Joseph Mwalichi; Ogallo, Laban; Opijah, Franklin; Nimusiima, Alex; Otieno, George; Ddumba, Saul Daniel; Nanteza, Jamiat; Basalirwa, Charles
    Variations in weather and climate have a significant impact on rain-fed banana yields in East Africa. This study examined empirical linkages between banana yields and variations in rainfalland temperature over Uganda for the historical period (1971-2009) using time series moments,correlation and regression analysis. The Food and Agriculture Organization (FAO) Crop Water Assessment Tool (CROPWAT) was used to estimate banana crop water requirements, soil moisturedeficits and their effects onbanana yield levels under rain-fed conditions for different regions. Thestudy observed high comparability in moment indices with some significant differences reflected in thevalues of the banana yields and rainfall and temperature moment indices. The cumulative effect ofrainfall and temperature variations on banana yields was discernible from strong correlationcoefficients of up to 78%. The CROPWAT simulations indicated up to 46% reductions in optimalbanana yields due to soil moisture deficits within banana plantations. In conclusion, the study observedstronger linkages between banana yields and temperature variations than rainfall. In addition,temperature manifests both direct and indirect effects on banana growth while rainfall exhibitscomparatively high intra-seasonal and intra-annual variability with lag effects on banana yields. Thestudy provides a strong scientific basis for the development of coping, adaptation and mitigationstrategies in the banana farming subsector in the region due to the anticipated shifts in rainfall and temperature extremes and changes across Uganda and neighbouring regions.
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    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.
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    Global GRACE Data Assimilation for Groundwater and Drought Monitoring: Advances and Challenges
    (Water Resources Research, 2019) Li, Bailing; Rodell, Matthew; Getirana, Augusto; Nanteza, Jamiat; Lannoy, Gabriëlle de; Bettadpur, Srinivas
    The scarcity of groundwater storage change data at the global scale hinders our ability to monitor groundwater resources effectively. In this study, we assimilate a state-of-the-art terrestrial water storage product derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into NASA's Catchment land surface model (CLSM) at the global scale, with the goal of generating groundwater storage time series that are useful for drought monitoring and other applications. Evaluation using in situ data from nearly 4,000 wells shows that GRACE data assimilation improves the simulation of groundwater, with estimation errors reduced by 36% and 10% and correlation improved by 16% and 22% at the regional and point scales, respectively. The biggest improvements are observed in regions with large interannual variability in precipitation, where simulated groundwater responds too strongly to changes in atmospheric forcing. The positive impacts of GRACE data assimilation are further demonstrated using observed low-flow data. CLSM and GRACE data assimilation performance is also examined across different permeability categories. The evaluation reveals that GRACE data assimilation fails to compensate for the lack of a groundwater withdrawal scheme in CLSM when it comes to simulating realistic groundwater variations in regions with intensive groundwater abstraction. CLSM-simulated groundwater correlates strongly with 12-month precipitation anomalies in low-latitude and midlatitude areas. A groundwater drought indicator based on GRACE data assimilation generally agrees with other regional-scale drought indicators, with discrepancies mainly in their estimated drought severity.
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    A Google Earth-GIS based approach to examine the potential of the current rainwater harvesting practices to meet water demands in Mityana district, Uganda
    (PLOS Water, 2022) Nanteza, Jamiat; Thomas, Brian; Kisembe, Jesse; Nakabugo, Rhoda; Mukwaya, Paul Isolo; Rodell, Mathew
    Rainwater harvesting (RWH) has become an integral part of global efforts to improve water access. Despite the increasing adoption of RWH in Uganda, there remains a significant knowledge gap in the assessment of RWH systems to meet water demands. In this study, a simplified methodology to estimate rainwater harvesting potential (RWHP) as a function of mean seasonal rainfall and rooftop area, generated using Google Earth and GIS tools is applied. Desired tank storage (DTS) capacities based on user population, demand and dry period lengths, were compared with RWHP to assess whether rooftop areas and tank storage can sustainably supply water for use during the March—May (MAM) and September-November (SON) 90-day dry periods, for three demand levels (i.e. for drinking and cooking (15 litres per capita per day (l/c/d)); for drinking, cooking and hand washing (20 l/c/d); and for drinking, cooking, hand washing, bathing and laundry (50 l/c/d)). Our findings document minimum catchment areas of 60m2 to have rainwater harvesting potential that can sustain households for 90-day dry periods for all three demand levels. However, considering their storage capacities, 25%, 48% and 97% of the existing RWHTs (with storage capacities below 8,000, 10,000 and 20,000 litres respectively) are unable to meet the demand of 15 l/c/d, 20 l/c/d and 50 l/c/d respectively for a 90-day dry period. The results document that the existing storage systems are under-sized for estimated water use under 50 l/c/d demand scenarios. Costs of between 2,000,000–4,500,000 Ugandan shillings (~ 600–1, 250 USD) would be needed to increase existing tank capacities to meet the 50 l/c/d demands for a 90-day dry period. These findings document onerous financial costs to achieve rainwater harvesting potential, meaning that households in Mityana district may have to resort to other sources of water during times of shortage.