Towards a framework of catchment classification for hydrologic
predictions and water resources management in the ungauged basin of the
Congo River: An a priori approach
Abstract
The Congo Basin exhibits tremendous heterogeneities, out of which it
emerges as an intricate system where complexity will vary consistently
over time and space. Increased complexity in the absence of adequate
knowledge will always result in increased uncertainties. One way of
simplifying this complexity is through an understanding of
organisational relationships of the landscape features, which is termed
here as catchment classification. The need for a catchment
classification framework for the Congo Basin is obvious given the
basin’s inherent heterogeneities, the ungauged nature of the basin, and
the pressing needs for water resources management that include the
quantification of current and future supplies and demands, which also
encompass the impacts of future changes associated with climate and land
use, as well as water resources operational policies. The need is also
prompted by many local-scale management concerns within the basin. This
study uses an a priori approach to determine homogenous
climatic-physiographic regions that are expected to underline dominant
hydrological processes characteristics. A set of 1740 catchment units
are partitioned across the whole basin, based on a set of comprehensive
criteria, including natural break of the elevation gradient (199 units),
inclusion of socio-economic and anthropogenic systems (204 units), and
water management units based on traditional nomenclature of the rivers
within the basin (1337 units). The identified catchment units are used
to assess existing datasets of the basin physical properties, necessary
to derive descriptors of the catchments characteristics. An unsupervised
classification, based on Hierarchical Agglomerative Cluster algorithm is
used, that yields 11 homogenous groups that are consistent with the
current perceptual understanding of the Congo Basin physiographic and
climatic settings. These regions represent therefore an a priori
classification that will be further used to derive functional
relationships of the catchments, necessary to enable hydrological
prediction and water management in the basin.