Modelling riverflow in the Volta Basin of West Africa: A data-driven framework (English shop)

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The 400,000 km2 Volta Basin is an international basin covering almost 28% of the West Coast.
Basin of Africa. It extends from longitude 5o 30 W to 2o 00E and from latitude 5 30o N to 14o 30
N and is shared by the six West African countries Benin, Togo, Ghana, La Cote d’Ivoire,
Burkina Faso and Mali. The water resources of this basin are under severe stress due to both
human and natural causes. High population growth rate coupled with widespread and
indiscriminate water mobilisation and use in the basin on the one hand, and high spatial and
temporal variability of rainfall and high potential evapotranspiration on the other, are putting
enormous pressure on the basin’s water resources. As a result, there are serious water resources
management problems such as flooding, water shortage, water pollution and loss of aquatic
biodiversity. It is, therefore, recognised that integrated transboundary water resources
management is necessary and urgent to ensure environmental integrity and sustainable water use
in the basin. Streamflow modelling and prediction are essential components of any water
resources management framework developed for the water-allocation and -use managers in the
basin.
In this thesis, a riverflow modelling framework developed for monthly riverflow
prediction in the Volta Basin is presented. By analysing available catchment rainfall, runoff and
potential evapotranspiration series in the basin using methods such as correlation plots,
autoregressive (AR) and autoregressive with exogenous input (ARX) modelling, it is shown that
the monthly catchment rainfall-runoff process is better characterised by non-linear models.
First, a spatio-temporal linear dynamic model employing the Kalman smoother and
the Expectation-Maximisation (EM) algorithm was developed and applied to filling in short
gaps in daily riverflow series in the basin. This model was found to be a very good and powerful
tool for filling in such data gaps.
Then, two non-linear modelling frameworks – a non-linear autoregressive and moving
average with exogenous input (NARMAX) polynomial and a data-based mechanistic (DBM)
modelling framework – were developed and applied to the monthly rainfall-runoff series in the
basin for river catchment runoff prediction. The NARMAX model was able to capture much of
the nonlinearity in the runoff generation process and provided good predictions of riverflow.
However, it is a purely black-box formulation providing no physical interpretation of the runoff
process in the basin. The DBM framework was very successful in representing the runoff
mechanism in the basin, adequately predicting monthly river runoffs. Unlike the NARMAX
models, the DBM framework is a grey-box that provided physically interpretable results at the
catchment scale. Results from this modelling framework show that monthly runoff in the basin
can be interpreted to occur in two pathways: a fast flow pathway and a slow, mainly delayed
flow, pathway. Catchment effective rainfall in the basin was found to have a power law
relationship with catchment runoff. In addition, the Identification of unit Hydrographs And
Component flows from Rainfall, Evaporation and Streamflow (IHACRES) type effective
rainfall-catchment wetness non-linear relationship in which the basin drying time constant is
exponentially related to basin potential evapotranspiration, was found to be suitable for
characterising the runoff processes in the basin.
Therefore, it is recommended that data-driven approaches be considered as the most
appropriate for riverflow modelling in the Volta Basin. This is due, in part, to the fact that the
approaches provide very good results that are, to some extent, physically interpretable and also
because the quality, quantity and diversity of hydrological data used for riverflow modelling in
the basin are too poor to enable effective use of the more elaborate distributed hydrological
models.