Assessment of Stream Hydrological Response Using Artificial Neural Network


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Hydrological alterations may result either from changes in average condition or from changes in the distribution and timing of extreme events. In view of this, the study attempted an evaluation of the
hydrological response of River Kaduna at Shiroro Dam site, Nigeria to hypothetical climate change scenarios using the Artificial Neural Network (ANN) paradigm. For the deployment of the ANN, monthly
historichydrometeorological data (i.e., evaporation, rainfall, streamflow and temperature) spanning 33 years were obtained. To this end, four climate change scenarios: +10% rainfall, 2×coefficient of variation
in rainfall, -10% rainfall and +30C average temperature were considered. The historical data were used as input to the ANN and selected monthly synthetic streamflow hydrographs in the seasons (i.e., dry and
wet) were generated with an average high value of the goodness-of-fit (R2=0.96). The response pattern indicated a variability index for the River to be in the range of 0.85-1.25 while for the recession pattern it is 0.75-0.81. It is imperative to note that the ANN enhanced the generalization of the flow dynamics of the extreme events (peak and low flow regime) with relative predictability capacity values of 103% ( ) and 96.35% ( ), respectively. However considering the fact that the upgraded temperature and coefficient of variation in rainfall might impact negatively on the average runoff, flow variability, flood frequency, and predictability, there is the need for the use of an extensive hydrometeorological database coupled with the application of associated risk value for effective flood forecasting in real-time.


In line with Alexi et al., (2007), any critical evaluation of the hydrological impact of climate change finds relevance against the backdrop of the need to plan for effective water resources management. Because of the importance of this subject, different methods have been employed
to assess the severity of the impact of climate change. Thus, regardless of uncertainty in a future climate, there are manifestations/features that there would be significant results on the water cycle and its environs (Merritt, et al., 2006). The water cycle rises when there is increasing
evaporation which in turn causes excessive rainfall (Zhang et al., 2007a and Ahn, et al., 2011). Rainfall intensity and amount vary with time and space and these changes have an either positive or negative sign on the water resource management (Ahn et al., 2011) thereby causing
hydrological response. In this context, therefore, the hydrological response of a stream is simply by the production of runoff against a given rainfall, which in turn is characterized by basin morphometric properties, soil characteristics and land use pattern (Ajibadeet al.,2010).


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Agricultural Engineering

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