Forecasting of Monthly Flow for the White Nile River (South Sudan)

A survey conducted in Terekeka, Mongalla (=Mongalla) and Gemmaiza (= Gemeiza), payams of Central Equatoria in South Sudan using face-to-face interviews, structured questionnaire and focused group discussion provided information on income generating strategies of fishing communities. These included: full time or part time fishing, small-scale farming, cattle breeding and firewood collection. Stationary gill nets were the dominant type of fishing gear, followed by monofilament, hook and long line, cast nets, spears and harpoons. Fishing vessels included planked canoes, steel boats and fibreglass. The best fishing months were August, September, followed by May. Main species caught included large bodied potamodromous predators adapted to channel habitats, as well as floodplain migrants. Overall the fish community appeared to be at equilibrium, with no evidence of impacts due to excessive catch efforts. The good health of the White Nile fishery is related to the high resilience of South Sudanese aquatic ecosystems as well as to the low potential of fish capture in a country disrupted by war and lack of security.

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The Role of Spatial Analysis in Detecting the Consequence of the Factory Sites : Case Study of Assalaya Factory-Sudan

E3S Web of Conferences ◽

10.1051/e3sconf/20183112004 ◽

2018 ◽

Vol 31 ◽

pp. 12004

Author(s):

Amar Sharaf Eldin Khair ◽

Purwanto ◽

Henna RyaSunoko ◽

Omer Adam Abdullah

Keyword(s):

Spatial Analysis ◽

Analysis Data ◽

Geographical Information ◽

Nile River ◽

Digital Elevation ◽

White Nile ◽

Elevation Model ◽

Factory Site

Spatial analysis is considered as one of the most important science for identifying the most appropriate site for industrialization and also to alleviate the environmental ramifications caused by factories. This study aims at analyzing the Assalaya sugarcane factory site by the use of spatial analysis to determine whether it has ramification on the White Nile River. The methodology employed for this study is Global Position System (GPS) to identify the coordinate system of the study phenomena and other relative factors. The study will also make use Geographical Information System (GIS) to implement the spatial analysis. Satellite data (LandsatDem-Digital Elevation Model) will be considered for the study area and factory in identifying the consequences by analyzing the location of the factory through several features such as hydrological, contour line and geological analysis. Data analysis reveals that the factory site is inappropriate and according to observation on the ground it has consequences on the White Nile River. Based on the finding, the study recommended some suggestions to avoid the aftermath of any factory in general. We have to take advantage of this new technological method to aid in selecting most apt locations for industries that will create an ambient environment.

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Using satellite data to identify the methane emission controls of South Sudan's wetlands

Biogeosciences ◽

10.5194/bg-18-557-2021 ◽

2021 ◽

Vol 18 (2) ◽

pp. 557-572

Author(s):

Sudhanshu Pandey ◽

Sander Houweling ◽

Alba Lorente ◽

Tobias Borsdorff ◽

Maria Tsivlidou ◽

...

Keyword(s):

Seasonal Cycle ◽

South Sudan ◽

Process Models ◽

Atmospheric Methane ◽

Methane Budget ◽

Order Of Magnitude ◽

Global Coverage ◽

Inundation Extent ◽

White Nile ◽

Total Column

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) provides observations of atmospheric methane (CH4) at an unprecedented combination of high spatial resolution and daily global coverage. Hu et al. (2018) reported unexpectedly large methane enhancements over South Sudan in these observations. Here we assess methane emissions from the wetlands of South Sudan using 2 years (December 2017–November 2019) of TROPOMI total column methane observations. We estimate annual wetland emissions of 7.4 ± 3.2 Tg yr−1, which agrees with the multiyear GOSAT inversions of Lunt et al. (2019) but is an order of magnitude larger than estimates from wetland process models. This disagreement may be explained by the underestimation (by up to 4 times) of inundation extent by the hydrological schemes used in those models. We investigate the seasonal cycle of the emissions and find the lowest emissions during the June–August season when the process models show the largest emissions. Using satellite-altimetry-based river water height measurements, we infer that this seasonal mismatch is likely due to a seasonal mismatch in inundation extent. In models, inundation extent is controlled by regional precipitation scaled to static wetland extent maps, whereas the actual inundation extent is driven by water inflow from rivers like the White Nile and the Sobat. We find the lowest emissions in the highest precipitation and lowest temperature season (June–August, JJA) when models estimate large emissions. In general, our emission estimates show better agreement in terms of both seasonal cycle and annual mean with model estimates that use a stronger temperature dependence. This suggests that temperature might be a stronger control for the South Sudan wetlands emissions than currently assumed by models. Our findings demonstrate the use of satellite instruments for quantifying emissions from inaccessible and uncertain tropical wetlands, providing clues for the improvement of process models and thereby improving our understanding of the currently uncertain contribution of wetlands to the global methane budget.

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Nile River (and White Nile)

Encyclopedia of Environment and Society ◽

10.4135/9781412953924.n768 ◽

2014 ◽

Keyword(s):

Nile River ◽

White Nile

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Diversity of selective and non-selective fishing gear and their impact on the White Nile River, Khartoum State, Sudan

African Journal of Environmental Science and Technology ◽

10.5897/ajest10.028 ◽

2011 ◽

Vol 5 (12) ◽

Author(s):

M. O. Mohammed

Keyword(s):

Nile River ◽

Fishing Gear ◽

White Nile

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Rapid adaptive radiation in a hillstream cyprinid fish in the East African White Nile River basin

Molecular Ecology ◽

10.1111/mec.16130 ◽

2021 ◽

Cited By ~ 1

Author(s):

Boris Levin ◽

Evgeniy Simonov ◽

Paolo Franchini ◽

Nikolai Mugue ◽

Alexander Golubtsov ◽

...

Keyword(s):

River Basin ◽

Adaptive Radiation ◽

Cyprinid Fish ◽

Nile River ◽

East African ◽

Nile River Basin ◽

White Nile

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Ethiopia, the Sudan, and Egypt: The Nile River Dispute

The Journal of Modern African Studies ◽

10.1017/s0022278x97002577 ◽

1997 ◽

Vol 35 (4) ◽

pp. 675-694 ◽

Cited By ~ 50

Author(s):

ASHOK SWAIN

Keyword(s):

Nile Blue ◽

River System ◽

Eastern Africa ◽

Nile River ◽

Seasonal Fluctuations ◽

Blue Nile ◽

North Eastern ◽

The World ◽

White Nile ◽

International River

The Nile flows for 6,700 kilometres through ten countries in north-eastern Africa – Rwanda, Burundi, Zaïre/Congo, Tanzania, Kenya, Uganda, Eritrea, Ethiopia, the Sudan, and Egypt – before reaching the Mediterranean, and is the longest international river system in the world – see Map 1. Its two main tributaries converge at Khartoum: the White Nile, which originates from Burundi and flows through the Equatorial Lakes, provides a small but steady flow that is fed by the eternal snows of the Ruwenzori (the ‘rain giver’) mountains, while the Blue Nile, which suffers from high seasonal fluctuations, descends from the lofty Ethiopian ‘water tower’ highlands. They provide 86 per cent of the waters of the Nile – Blue Nile 59 per cent, Baro-Akobo (Sobat) 14 per cent, Tekesse (Atbara) 13 per cent – while the contribution from the Equatorial Lakes region is only 14 per cent.

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Prevalence, risk factors, and clinical manifestations of schistosomiasis among school children in the White Nile River basin, Sudan

Parasites & Vectors ◽

10.1186/s13071-014-0478-6 ◽

2014 ◽

Vol 7 (1) ◽

Cited By ~ 22

Author(s):

Hassan Ahmed Hassan Ahmed Ismail ◽

Sung-Tae Hong ◽

Azza Tag Eldin Bashir Babiker ◽

Randa Mohamed Abd Elgadir Hassan ◽

Mohammed Ahmed Zakaria Sulaiman ◽

...

Keyword(s):

Risk Factors ◽

River Basin ◽

School Children ◽

Clinical Manifestations ◽

Nile River ◽

Nile River Basin ◽

White Nile

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Bahr el Ghazal: Nile River Basin (Sudan and South Sudan)

The Wetland Book ◽

10.1007/978-94-007-6173-5_215-1 ◽

2016 ◽

pp. 1-9

Author(s):

A. I. el Moghraby

Keyword(s):

River Basin ◽

South Sudan ◽

Nile River ◽

Nile River Basin

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Retrospective Analysis and Bayesian Model Averaging of CMIP6 Precipitation in the Nile River Basin

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0157.1 ◽

2020 ◽

Author(s):

Mohammed Ombadi ◽

Phu Nguyen ◽

Soroosh Sorooshian ◽

Kuo-lin Hsua

Keyword(s):

Retrospective Analysis ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

Model Averaging ◽

Annual Precipitation ◽

Nile River ◽

Nile Basin ◽

White Nile ◽

Projected Changes ◽

Precipitation Projections

The Nile river basin is one of the global hotspots vulnerable to climate change impacts due to fast growing population and geopolitical tensions. Previous studies demonstrated that general circulation models (GCMs) frequently show disagreement in the sign of change in annual precipitation projections. Here, we first evaluate the performance of 20 GCMs from the 6th Coupled Model Intercomparison Project (CMIP6) benchmarked against a high spatial resolution precipitation dataset dating back to 1983 from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR). Next, a Bayesian Model Averaging (BMA) approach is adopted to derive probability distributions of precipitation projections in the Nile basin. Retrospective analysis reveals that most GCMs exhibit considerable (up to 64% of mean annual precipitation) and spatially heterogenous bias in simulating annual precipitation. Moreover, it is shown that all GCMs underestimate interannual variability; thus, the ensemble range is under-dispersive and a poor indicator of uncertainty. The projected changes from the BMA model show that the value and sign of change varies considerably across the Nile basin. Specifically, it is found that projected changes in the two headwaters basins, namely Blue Nile and Upper White Nile are 0.03% and -1.65% respectively; both statistically insignificant at α = 0.05. The uncertainty range estimated from the BMA model shows that the probability of a precipitation decrease is much higher in the Upper White Nile basin whereas projected change in the Blue Nile is highly uncertain both in magnitude and sign of change.

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