Browsing by Author "Mbogga, Michael"

Now showing 1 - 5 of 5
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    A Comprehensive Set of Interpolated Climate Data for Alberta
    (Alberta Sustainable Resource Development, 2010) Mbogga, Michael; Wang, Tongli; Hansen, Christine; Hamann, Andreas
    We present an easily accessible database of interpolated climate data for Alberta that includes monthly, annual, decadal, and 30-year normal climate data for the last 106 years (1901 to 2006), as well as climate change projections for the 21st century from 23 general circulation models. The database builds on the Alberta Climate Model (Alberta Environment 2005) and a set of five future projections that are recommended and widely used by Alberta government agencies (Barrow and Yu 2005). We added 15,000 historical and projected climate surfaces that include variables relevant for biological research and infrastructure planning, such as growing and chilling degree days, heating and cooling degree days, growing season length descriptors, frost free days and extreme minimum temperature. The database can be queried through a provided software package ClimateAB. A representative subset of these climate surfaces has been thoroughly checked against observed weather station data. We report error estimates for historical climate data and discuss the strengths and limitations of this database for use by natural resource managers and researchers.
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    Conservation of Genetic Resources of Non-Timber Forest Products in Ethiopia
    (National Workshop on Non-Timber Forest Products, 2004) Tadesse, Wubalem; Mbogga, Michael
    During the last 10–20 years, human interest in non-timber forest products (NTFPs) that appeared relevant to the growing focus on rural development and conservation of natural resources has grown (Arnold and Perez, 2001). Indeed, NTFPs seemed to offer hope that their presence in the forest would act as an incentive to conserve the forest (Lawrence, 2003), at the same time contributing to community development. This was based on the perception that these products are more accessible to rural populations and especially to the rural poor (Saxena, 1995), and that their exploitation is more benign than timber harvesting (Myers, 1988). Moreover, there is an assumption, often implicit, that making forests more valuable to local users can encourage forest conservation (Plotkin and Famolare, 1992; Evans 1993). It is now widely accepted that this has not been the case in many situations, calling for a redress in our approach to NTFPs conservation and use. Despite the fact that NTFPs contribute to tropical forest conservation and poverty alleviation was regarded as very promising, recent studies have cleared, however, that the alleged commercialization-conservation/development link in the NTFPs debate needs reconsideration (Ros-Tonen and Wiersum, 2004). The exploitation of forest resources has a differentiated effect, depending on the type of species and the parts being harvested (Arnold and Perez, 2001). The effect of uncontrolled exploitation of NTFPs from natural population can also have adverse effects not only on the species exploited but also on other associated species. This is why approaches to conservation of NTFPs source species that are sources of non–timber forest products need to be tailored to individual species and areas. Although some NTFPs play a role in rural livelihood strategies and can contribute to sustain forested landscapes in various tropical forest areas, there is no uniform picture as regards the actual importance of NTFPs to rural livelihoods (Ros-Tonen and Wiersum, 2004).
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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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    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.
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    Trees and Watershed Management in Karamoja, Uganda
    (Evidence on Demand, 2014) Mbogga, Michael; Malesu, Maimbo; de Leeuw, Jan
    Karamoja is a dryland sub-region in north-east Uganda. Having suffered historical injustices, it now faces many difficulties, including civil and administrative challenges. Karamoja performs poorly on development indicators compared to other parts of Uganda: 82% of its population lives under the poverty line. Its infrastructure is underdeveloped, and the subregion is troubled by climate variability and climate change. Drought and shifts in weather result in low agricultural productivity and declining rural production systems. Floods and droughts have had a particularly detrimental effect. Moreover, Karamoja faces increasing environmental degradation, further threatening crop and livestock production. Trees are at the heart of Karamoja’s ecology, providing livelihoods and nutrition for livestock and people when all else fails; trees also provide Karamoja with fundamental ecosystem services. Thus there is a need for evidence about the role that trees play in Karamoja. This document looks at trees in watershed management in the sub-region. Efficient water management may provide a large part of the solution to the current poor livelihood prospects in Karamoja. From consultation with experts and a literature review, there is wide evidence of the benefits that trees confer to communities in Karamoja. We see various options for action with respect to trees in watershed management: the use of trees for flash flood control; erosion control and waterway fixation; resilient crop production; resilient livestock production; and efficient utilization of green water -- the precipitation that falls on the land, which does not run off into rivers, dams or groundwater but is absorbed into the soil. Karamoja experiences frequent flash floods caused by water from heavy rains running from higher to lower lying areas. These can devastate lives and property, often sweeping away houses and farmlands. Ground-covering vegetation and trees can significantly reduce occurrence of flash floods. Trees allow for the infiltration of water into the soil. Therefore, this review strongly advises higher tree coverage in Karamoja’s crop fields and rangelands. Another benefit of trees is that they reduce erosion. They intercept rainfall, reducing the force with which drops strike the soil. Rainfall on bare land makes soil compact. The pores in the soil, which normally absorb the water, close; as a result, rainfall, instead of soaking into the soil, turns into runoff that often carries away valuable top soil, silting up streams, rivers and dams. This, in turn, harms the proper streaming of water. This review strongly recommends the maintenance, planting and regeneration of trees along riverbanks to control erosion. Water management focuses on availability of blue water, the fresh surface and groundwater found in lakes, rivers or aquifers. While blue water is important, this review advises that green water is equally important. Most rainwater that falls goes to the creation of biomass. Green water is especially valuable for crop growth and livestock production, since it is easily taken up by biomass through the soil. The use of trees needs to be mainstreamed in watershed management planning. Currently, many water resource management plans exist. An objective should be that watershed management organizations include trees in their planning. We advise that DFID develop capacity in organizations responsible for water management.