Abstract

Catchment classification is one approach in natural resource management that is widely adopted in taking efficient steps towards implementing suitable soil and water conservation measures across a basin or region. Catchments have unique characteristics emerging from the heterogeneity and complexity of the systems and classifying them paves way to achieve order and simplicity. However, some constraints related to data availability could be a problem in a region where only few rivers are gauged and with only one type of climate data available. This study presents a way to decrease complexity by grouping these catchments based on their biophysical characteristics extracted from readily available datasets and using simple statistical approaches. Principal component analysis was first conducted to twenty-four biophysical variables which were reduced to eight factor components. A hierarchical clustering method was then performed to define the number of clusters and K-means clustering procedure was followed for the final grouping. Nine watershed clusters were formed with watershed size having the greatest contribution. Grouping catchments into clusters with similar biophysical characteristics does not only promote simplicity but also facilitates understanding of the nature of not only one watershed but also its relationship with other watersheds in a bigger landscape. The study also confirmed that spatially close watersheds exhibit similar characteristics.

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