Modelling the dry season inundation pattern of Yala Swamp in Kenya

Abstract

Ecological processes in wetlands rely on the natural variability of inundation cycle guided by different seasons. Human-induced modifications have, however, influenced the seasonality and the period of inundation, altering its inherent variability. The adjustments have further affected the functioning of many global wetlands by shifting their natural equilibrium. In a quest to understand the wetland’s inundation pattern, cellular automata and Markov chain analysis (CA–MCA) model was used to simulate the 2029 dry season inundation variation of Yala Swamp in Kenya. The model utilised parameters obtained from Tasselled Cap Wetness Index, supervised classification, population growth model and elevation for the years 1986, 2006, 2015 and 2019. The study established that inundation characteristics in the swamp varied as the years progressed from 1986 to 2019. Consistency was found between inundation variation and changes in land-use practices. Consequently, the area covered by the wetland decreased in its spatial extent by approximately 35%. The model projected that the swamp would additionally lose 12% of its inundated area by 2029, should the current practices continue. It is therefore essential to regulate anthropogenic activities to control the variability in the swamps inundation.