Prediction of the impacts of climate changes on the stream flow of Ajali River Watershed using SWAT model

Obasi, A. I.; Ogbu, K. N.; Ndulue, Emeka; Ogwo, VN; Mbajiorgu, C.C.

doi:10.4314/njt.v36i4.39

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Industries and cities have historically been located along rivers because the rivers provide transportation and have traditionally been convenient places to discharge waste. Domestic and industrial discharges to rivers are one of the causes of river pollution [2].…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Physiochemical Properties of Ajali River Stretch in Enugu, Enugu State Nigeria

Nwerem¹,

Ejikeme²,

Ekere³

et al. 2023

JCSN

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This work studied the physiochemical properties of Ajali River polluted with Industrial effluent at different sampling points from -250m upstream before the pollution to 2250m downstream. Statistical package for Social Science (SPSS) was used for the statistical analysis. Post ANOVA test was used to determine if there is any significant difference between the means and at which distance the difference occurred. A measuring wheel and Global Positioning System (GPS) were used for the exact locations studied. The azide-Winkler method was used for DO determination, while the five-day incubation method was used for BOD. It was observed that there was a significant difference (p˂0.005) between the means of the properties across the sampling points. Dissolve Oxygen was found to be higher in the rainy season with a range of 3.82±0.47 to 7.70±0.40. Biochemical Oxygen demand was equally higher in the rainy season with a range of 2.05±0.02 to 5.02±0.41. Chemical Oxygen demand was higher in the rainy season with a range of 6.20±0.12 to 15.04±0.03. It was observed that the effect of the pollution decreased along the sampling points away from the point of discharge to the stream suggesting that the river has a natural tendency to purify itself.

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Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Physiochemical Properties of Ajali River Stretch in Enugu, Enugu State Nigeria

Nwerem¹,

Ejikeme²,

Ekere³

et al. 2023

JCSN

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“…Several findings indicate that potential climate variability would lead to heavy precipitation. This could contribute to more floods, especially as a channel adapts to the different stream flow conditions [1].…”

Section: Introductionmentioning

confidence: 99%

Modeling the Influence of Meteorological Variables on Runoff in a Tropical Watershed

Ekwueme

Agunwamba

2020

Civ Eng J

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Proper understanding of the historical annual runoff characteristics with respect to climate impacts is essential for effective planning as well as the management of water resources in river basins. In this study, the climate-flood model which connects the runoff and climate was developed for Adada River Nigeria. Thirty years records of climatic and runoff data were used to develop a multiple linear regression model. The coefficient of determination was evaluated for the developed model, and the hypothesis was equally tested with the aid of t-test and one-way analysis of variance. The multiple regression analysis indicated that the climate-flood model was statistically signiﬁcant (p˂0.05) in predicting the annual runoff. The results also show that the climatic variables accounted for 66.1% of runoff variation due to the undisturbed gauging basin of the river. The wind speed and the duration of sunlight were not statistically signiﬁcant predictors of runoff in the area. These results, obtained signify that climate has a major impact on runoff and it could help in understanding the availability of water within the catchment area. Doi: 10.28991/cej-2020-03091621 Full Text: PDF

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“…Efforts by the Nigerian Federal Ministry of Water Resources through the Nigerian Hydrological Services Agency (NHISA) to forecast flow processes of major rivers in Nigeria for early flood risk warning have yielded low success rate because of reliance on deterministic modelling approach. Paucity of hydro-metrological gauge networks in Nigeria has majorly impacted on deterministic model outputs and affected scientific model-based policies [OBASI et al 2017].…”

Section: Introductionmentioning

confidence: 99%

Journal of Water and Land Development

Obasi

Ogbu

Orakwe

et al. 2019

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This study is aimed at evaluating the applicability of Artificial Neural Network (ANN) model technique for river discharge forecasting. Feed-forward multilayer perceptron neural network trained with back-propagation algorithm was employed for model development. Hydro-meteorological data for the Imo River watershed, that was collected from the Anambra-Imo River Basin Development Authority, Owerri -Imo State, South-East, Nigeria, was used to train, validate and test the model. Coefficients of determination results are 0.91, 0.91 and 0.93 for training, validation and testing periods respectively. River discharge forecasts were fitted against actual discharge data for one to five lead days. Model results gave R 2 values of 0.95, 0.95, 0.92, 0.96 and 0.94 for first, second, third, fourth, and fifth lead days of forecasts, respectively. It was generally observed that the R 2 values decreased with increase in lead days for the model. Generally, this technique proved to be effective in river discharge modelling for flood forecasting for shorter lead-day times, especially in areas with limited data sets.

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Prediction of the impacts of climate changes on the stream flow of Ajali River Watershed using SWAT model

Cited by 7 publications

References 4 publications

Statistical Analysis of Physiochemical Properties of Ajali River Stretch in Enugu, Enugu State Nigeria

Statistical Analysis of Physiochemical Properties of Ajali River Stretch in Enugu, Enugu State Nigeria

Modeling the Influence of Meteorological Variables on Runoff in a Tropical Watershed

Journal of Water and Land Development

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