Mathematical Models of Seasonal Dynamics of Bedload Sediments in Eco-Geomorphologic Units of the Tropical Rivers, Southeastern Nigeria
DOI:
https://doi.org/10.37933/nipes/3.4.2021.26Abstract
The application of valid mathematical models of natural phenomena
in space among geographers tends to impede testing and clarifying
complex associations and variances among the sets of variables. Such
information is vital for sound ecosystem and engineering policy/
decision-making toward the protection of fragile and endangered
hydrological and geomorphological units in the future. This study
adopted the direct field survey and laboratory techniques. Using
stratified and systematic sampling methods, eight ecogeomorphological sub-units comprising one first-order stream, six
fourth-order tributaries, and the estuary were selected. A total of 32
bedload samples were systematically collected during the four
climatic seasons, digest properly to ensure standard compliance, and
analyzed in the laboratory. The MANOVA tests of variations,
overlapping variances, and homogeneities among the groups of
parameters gave Pillai’s Trace (2.027), Wilks’ Lambda (24.745),
Hotelling’s Trace (6139.576), and Roy's Largest Root (40035.113)
each significant at 0.00 confidence level. The results implied that
variations in eco-geomorphologic units and climatic seasons have a
significant effect on the dispersal of bedload sediments within the
study area. Also, the prevalence of flood and erosion hazards in the
area has strong affinity with natural forces and anthropocentric
parasitism, with serious threats to the future of the regional
ecosystem. This study recommended: (i) sustainable and deliberate
promotions of community-driven afforestation programmes with
strong supports from the governments and donor agencies to facilitate
ecosystem services and mitigate the impacts of climate that induced
geomorphic hazards in the area and, (ii) periodic dredging of silted
small rivers and construction of drainages/ roads to regulate surface
runoff/ discharge from cities to rivers.