Abstract
The dynamics and accurate forecasting of streamflow processes of a river are important in the management of extreme events such as floods and droughts, optimal design of water storage structures and drainage networks. In this study, attempt was made at investigating the appropriateness of stochastic modelling of the streamflow process of the Benue River using data-driven models based on univariate streamflow series. To this end, multiplicative seasonal Autoregressive Integrated Moving Average (ARIMA) model was developed for the logarithmic transformed monthly flows. The seasonal ARIMA model’s performance was compared with the traditional Thomas-Fiering model forecasts, and results obtained show that the multiplicative seasonal ARIMA model was able to forecast flow logarithms. However, it could not adequately account for the seasonal variability in the monthly standard deviations. The forecast flow logarithms therefore cannot readily be transformed into natural flows; hence, the need for cautious optimism in its adoption, though it could be used as a basis for the development of an Integrated Riverflow Forecasting System (IRFS). Since forecasting could be a highly “noisy” application because of the complex river flow system, a distributed hydrological model is recommended for real-time forecasting of the river flow regime especially for purposes of sustainable water resources management.
Highlights
Inherent in the principles of water resources management is the judicious utilization and conservation of the available water resources
The objective of this study is to model the streamflow process of the Benue River with Autoregressive Integrated Moving Average (ARIMA) models, focusing on short term forecasting for the purposes of evaluating suitability of particular model type as a preliminary step towards developing an enhanced “River Flow Forecasting System” for the river
The ARIMA model was able to forecast flow logarithms, but because it did not adequately account for the seasonal variability in the monthly standard deviations, the standard errors associated with the forecasts may not be physically correct
Summary
Inherent in the principles of water resources management is the judicious utilization and conservation of the available water resources. One of the ways to enhance this is the proper estimation of water demand both quantitatively and qualitatively. Within this overall management system, the hydrologist is often required to estimate the magnitude of extreme events, whereas operation of some of the design works is often dependent on reliable estimates of flow for an ensuing period of time. The available streamflows, known as historical records, are often quite short, generally sometimes less than a quarter of a century in length. A system designed on the basis of the historical record only faces a chance of being inadequate for the unknown flow sequence that the system might experience. The reliability of a system has to be evaluated under these conditions which are not possible with historical records alone
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