Author(s)

Wahab A. Iddrisu^1*, Kaku S. Nokoe¹, Albert Luguterah², Eric O. Antwi³

¹Department of Mathematics and Statistics, University of Energy and Natural Resources, Sunyani, Ghana.
²Department of Statistics, University for Development Studies, Navrongo, Ghana.
³Department of Energy and Environmental Engineering, University of Energy and Natural Resources, Sunyani, Ghana.

River discharge data offer a rich source of information for reservoir management and flood control, if modelling can separate out the effects of rainfall, land use, soil type, relief, and weather conditions. In this paper, we model river discharge data from the Black Volta River, using Generalised Additive Mixed Models (GAMMs) with a space-time interaction represented via a tensor product of continuous time and discrete space. River discharge data from January 2000 to December 2009 for the four gauge stations along the Black Volta River namely, Lawra, Chache, Bui and Bamboi were obtained from the hydrological services department of Ghana and used for model fitting. Four GAMMs were explored, two with space-time interactions and two without space-time interactions. The comparison of the performance of the models with space-time interactions and those without space-time interactions based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) suggests that in this application, the former is better overall and in particular for modelling local variations. Further, a model with space and time main effects performed better compared with one without space and time main effects. After model selection, checking and validation, there is evidence for increasing river discharge from the most upstream gauge station to the most downstream gauge station for the study period.

River Discharge, GAMM, Tensor Product Smooth, Space-Time Interaction, Black Volta River

Iddrisu, W. , Nokoe, K. , Luguterah, A. and Antwi, E. (2017) Generalized Additive Mixed Modelling of River Discharge in the Black Volta River. Open Journal of Statistics, 7, 621-632. doi: 10.4236/ojs.2017.74043.