Physical dynamics of Lake Victoria over the past 34 years (1984–2018): Is the lake dying?

Awange, Joseph; Saleem, Ashty; Sukhadiya, R.; Ouma, Yashon O.; Kexiang, H.

doi:10.1016/j.scitotenv.2018.12.051

Cited by 53 publications

(47 citation statements)

References 83 publications

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“…In the semi-arid ecosystems of sub-Saharan Africa, ecosystem productivity is strongly linked to soil moisture (Madani et al, 2017). Studies of sub-Saharan cattle have shown that animal weights and dry matter intake vary seasonally, linked to the availability of forage (Ayantunde et al, 2005;Assouma et al, 2018). As such, the seasonal variation of enteric fermentation CH 4 emissions from livestock could be a significant contributor to the seasonal cycle of total CH 4 emissions that we infer.…”

Section: Seasonal Variations Of African Ch 4 Emissionsmentioning

confidence: 71%

An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data

et al. 2019

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Abstract. Emissions of methane (CH4) from tropical ecosystems, and how they respond to changes in climate, represent one of the biggest uncertainties associated with the global CH4 budget. Historically, this has been due to the dearth of pan-tropical in situ measurements, which is particularly acute in Africa. By virtue of their superior spatial coverage, satellite observations of atmospheric CH4 columns can help to narrow down some of the uncertainties in the tropical CH4 emission budget. We use proxy column retrievals of atmospheric CH4 (XCH4) from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the nested version of the GEOS-Chem atmospheric chemistry and transport model (0.5∘×0.625∘) to infer emissions from tropical Africa between 2010 and 2016. Proxy retrievals of XCH4 are less sensitive to scattering due to clouds and aerosol than full physics retrievals, but the method assumes that the global distribution of carbon dioxide (CO2) is known. We explore the sensitivity of inferred a posteriori emissions to this source of systematic error by using two different XCH4 data products that are determined using different model CO2 fields. We infer monthly emissions from GOSAT XCH4 data using a hierarchical Bayesian framework, allowing us to report seasonal cycles and trends in annual mean values. We find mean tropical African emissions between 2010 and 2016 range from 76 (74–78) to 80 (78–82) Tg yr−1, depending on the proxy XCH4 data used, with larger differences in Northern Hemisphere Africa than Southern Hemisphere Africa. We find a robust positive linear trend in tropical African CH4 emissions for our 7-year study period, with values of 1.5 (1.1–1.9) Tg yr−1 or 2.1 (1.7–2.5) Tg yr−1, depending on the CO2 data product used in the proxy retrieval. This linear emissions trend accounts for around a third of the global emissions growth rate during this period. A substantial portion of this increase is due to a short-term increase in emissions of 3 Tg yr−1 between 2011 and 2015 from the Sudd in South Sudan. Using satellite land surface temperature anomalies and altimetry data, we find this increase in CH4 emissions is consistent with an increase in wetland extent due to increased inflow from the White Nile, although the data indicate that the Sudd was anomalously dry at the start of our inversion period. We find a strong seasonality in emissions across Northern Hemisphere Africa, with the timing of the seasonal emissions peak coincident with the seasonal peak in ground water storage. In contrast, we find that a posteriori CH4 emissions from the wetland area of the Congo Basin are approximately constant throughout the year, consistent with less temporal variability in wetland extent, and significantly smaller than a priori estimates.

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Section: Seasonal Variations Of African Ch 4 Emissionsmentioning

confidence: 71%

An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data

et al. 2019

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“…The novelty of this present contribution, therefore, is that it extends on the work of Hagenaars et al (2018) by providing the first test of the applicability of manually digitised coastline from Sentinel-2 products to monitor coastline movements over data deficient regions, thereby providing high accurate spatio-temporal mapping than previously achieved using freely available remotely sensed Landsat products. The advantages of using manually digitised imagery include, e.g., avoiding misclassification (see Awange et al, 2019;Awange et al, 2018;Dewan et al, 2017). This becomes important for inaccessible regions where obtaining in-situ data for validating automated imageries is impossible as is the case for Somalia.…”

Section: Tablementioning

confidence: 99%

Coastline shift analysis in data deficient regions: Exploiting the high spatio-temporal resolution Sentinel-2 products

Saleem

Awange

2019

CATENA

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In most developing countries, coastline shift monitoring using in-situ (ground-based) data faces challenges due, e.g., to data unreliability, inconsistency, deficiency, inaccessibility or incompleteness. Even where practically applicable, the traditional "boots on the ground" methods are labour intensive and expensive, thus imposing burden on poor countries struggling to meet other urgent pressing daily needs, i.e., food and medicine. Remote sensing (RS) techniques provide a more efficient and effective way of collecting data for coastline shift analysis. However, moderate spatio-temporal resolution RS products such as the widely used Landsat products (30 m and 16 days) may be insufficient where high accuracy is desired. In 2015, Sentinel-2 Multi-Spectral Instrument (MSI) remotely sensed products with higher spatio-temporal resolution (10 m and 5 days) and high spectral resolution (13 bands), which promises to improve coastline movement monitoring to high accuracy, was launched. Using two war-impacted countries (Liberia and Somalia) as case studies of regions with data deficiency or of poor quality, for the period 2015-2018, this contribution aims at (i) assessing the suitability of the new freely available high spatio-temporal Sentinel-2 products to monitor coastline shift, (ii) assessing the possibility of filling the missing Sentinel-2 gaps with Landsat 8 panchromatic band (15 m) products to provide alternative data source for mapping of coastline movements where Sentinel-2 data is unusable, e.g., due to cloud cover, and (iii), undertake a comparative analysis between Sentinel-2 (10 m), Landsat panchromatic (15 m), and Landsat multi-spectral (30 m). The results of the evaluation indicate 23% (on average) improvement gained by using Sentinel-2 compared to the traditional Landsat 30 m resolution data (i.e., 32% for Liberia and 14% for Somalia). A comparison of 100 check points from Google Earth Pro (i.e., surrogate in-situ reference data) show 91% agreement for Liberia and 85% for Somalia, indicating the potential of using Sentinel-2 data for future coastal shift studies, particularly for the data deficient regions. The results of comparative studies for Sentinel-2, Landsat panchromatic (PAN), and Landsat multi-spectral (MS) show that the percentages of Sentinel-2 and Landsat PAN that falls within 10 m threshold is much higher than Landsat MS by 35% and 26%, respectively, and for the 2016-2017 period, they provide more detailed mapping of the Liberian coastline compared to Landsat MS (30 m). Finally, panchromatic Landsat data with 15 m resolution are found to be capable of filling the missing Sentinel-2 gaps, i.e., where cloud cover hampers its usability.

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“…Lake Victoria, the world’s second largest freshwater lake that supports over 42 million people within its basin (e.g., [ 1 ]) has recently undergone unprecedented changes caused by multiple factors such as heavy rains, human activities, environmental degradation, and urbanization, which have led to a significant water level rise (e.g., [ 2 , 3 ]). In the past two years (2019–2020), the lake’s water level increased remarkably to an all-time record that resulted in floods, impacts on drinking water and sanitation systems, increased water-related diseases, and impacts on hydropower infrastructures [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…In the past two years (2019–2020), the lake’s water level increased remarkably to an all-time record that resulted in floods, impacts on drinking water and sanitation systems, increased water-related diseases, and impacts on hydropower infrastructures [ 4 ]. In addition to providing water resources, the lake supports the region’s agriculture, fisheries, and hydropower, and is also known to modulate the regional climate (see, e.g., [ 1 , 5 ]). These vital roles played by the lake suggest the fact that its short to long-term changes can significantly impact people’s livelihood in the region and beyond (see, e.g., [ 2 ]).…”

Section: Introductionmentioning

confidence: 99%

The 2019–2020 Rise in Lake Victoria Monitored from Space: Exploiting the State-of-the-Art GRACE-FO and the Newly Released ERA-5 Reanalysis Products

Khaki

Awange

2021

Sensors

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During the period 2019–2020, Lake Victoria water levels rose at an alarming rate that has caused various problems in the region. The influence of this phenomena on surface and subsurface water resources has not yet been investigated, largely due to lack of enough in situ measurements compounded by the spatial coverage of the lake’s basin, incomplete/inconsistent hydrometeorological data, and unavailable governmental data. Within the framework of joint data assimilation into a land surface model from multi-mission satellite remote sensing, this study employs the state-of-art Gravity Recovery and Climate Experiment follow-on (GRACE-FO) time-variable terrestrial water storage (TWS), newly released ERA-5 reanalysis, and satellite radar altimetry products to understand the cause of the rise of Lake Victoria on the one hand, and the associated impacts of the rise on the total water storage compartments (surface and groundwater) triggered by the extreme climatic event on the other hand. In addition, the study investigates the impacts of large-scale ocean–atmosphere indices on the water storage changes. The results indicate a considerable increase in water storage over the past two years, with multiple subsequent positive trends mainly induced by the Indian Ocean Dipole (IOD). Significant storage increase is also quantified in various water components such as surface water and water discharge, where the results show the lake’s water level rose by ∼1.4 m, leading to approximately 1750 gigatonne volume increase. Multiple positive trends are observed in the past two years in the lake’s water storage increase with two major events in April–May 2019 and December 2019–January 2020, with the rainfall occurring during the short rainy season of September to November (SON) having had a dominant effect on the lake’s rise.

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Physical dynamics of Lake Victoria over the past 34 years (1984–2018): Is the lake dying?

Cited by 53 publications

References 83 publications

An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data

An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data

Coastline shift analysis in data deficient regions: Exploiting the high spatio-temporal resolution Sentinel-2 products

The 2019–2020 Rise in Lake Victoria Monitored from Space: Exploiting the State-of-the-Art GRACE-FO and the Newly Released ERA-5 Reanalysis Products

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Physical dynamics of Lake Victoria over the past 34 years (1984–2018): Is the lake dying?

Cited by 53 publications

References 83 publications

An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data

An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data

Coastline shift analysis in data deficient regions: Exploiting the high spatio-temporal resolution Sentinel-2 products

The 2019–2020 Rise in Lake Victoria Monitored from Space: Exploiting the State-of-the-Art GRACE-FO and the Newly Released ERA-5 Reanalysis Products

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Physical dynamics of Lake Victoria over the past 34 years (1984–2018): Is the lake dying?