Modelling the dry season inundation pattern of Yala Swamp in Kenya

Wanjala, Johanna Anyesi; Sichangi, Arthur Wafula; Mundia, Charles N.; Makokha, Godfrey Ouma

doi:10.1007/s40808-020-00816-8

Cited by 9 publications

(2 citation statements)

References 24 publications

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“…To predict the lake extent map for 2022, the study input the generated transition suitability image and Markov transition area les, along with the 2012 lake extent map, into the Cellular Automata/Markov chain predicting tool. The number of Cellular Automata iterations was set to 10, and a standard 5×5 contiguity Cellular Automata (CA) lter was used to determine the weight factor of transition (Wanjala et al 2020). Validation of the predicted lake extent map for 2022 involved a comparison with the simulated lake extent map for the same year.…”

Section: Lake Extent Prediction Using the Cellular Automata-markov Ch...mentioning

confidence: 99%

Spatio-temporal modeling of lake’s ecosystem and dynamism in response to changing environment. A case study of L. Olbolossat in Kenya

Nyambane,

Kimwatu

2024

Preprint

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Lakes’ ecosystems are vulnerable to environmental dynamisms prompted by natural processes and anthropogenic activities happening in catchment areas. The present study aimed at modeling the response of Lake Olbolossat ecosystem in Kenya to changing environment between 1992 to 2022, and its future scenario in 2030. The study used temperature, stream power index, rainfall, land use land cover, normalized difference vegetation index, slope and topographic wetness index as datasets. A GIS-ensemble modeling approach coupling the analytical hierarchical process and principal component analysis was used to simulate the lake’s extents between 1992–2022. Cellular Automata-Markov chain analysis was used to predict the lake extent in 2030. The results revealed that between 1992–2002, the lake extent shrunk by about 18%; between 2002–2012, the lake extent increased by about 13.58%; and between 2012–2022, the lake expanded by about 26%. The spatial temporal changes exhibited that the lake has been changing haphazardly depending on prevailing climatic conditions and anthropogenic activities. The comparison between the simulated and predicted lake extents in 2022 produced Kno, Klocation, KlocationStrata, Kstandard, and average index values of 0.80, 0.81, 1.0, 0.74, and 0.84, respectively, which ascertained good performance of generated prediction probability matrices. The predicted results exhibited there would be an increase in lake extent by about 13% by the year 2030. The research findings provide baseline information which would assist in protecting and conserving the lake Olbolossat ecosystem which is very crucial in promoting tourism activities and provision of water for domestic and commercial use in the region.

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Section: Lake Extent Prediction Using the Cellular Automata-markov Ch...mentioning

confidence: 99%

Spatio-temporal modeling of lake’s ecosystem and dynamism in response to changing environment. A case study of L. Olbolossat in Kenya

Nyambane,

Kimwatu

2024

Preprint

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“…These studies highlighted that a decline in wetland areas have been mainly attributed to an increase in the built up areas. Other studies revealed that unprotected wetlands are highly vulnerable to environmental and anthropogenic changes through alterations of hydrological regimes, which threaten wetland areas and their-dependent species (Al-Obaid et al, 2017;Bhanga et al, 2020;Wanjala et al, 2020). Natural processes including change in temperature, rainfall patterns, evapotranspiration, drought rate and erosion have fast-tracked wetland water losses (Xia et al, 2017;Chen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

An Assessment of Small Wetland Ecohydrological Dynamics Using Sentinel-2 MSI Derived Spectral Indices in Semi-Arid Environments of South Africa

Thamaga

Dube

Shoko

2021

Preprint

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Water presence within small wetlands serve as a determining factor influencing biodiversity productivity and wetland functionality. Small wetlands remain largely unprotected hence, they are more sensitive to frequent exposure to environmental modifications, and are less resilient to changing rainfall patterns, climate change and variability, droughts and changing land use practices. Accurate and up to date spatial and temporal information on changes in surface water and inundation extent becomes imperative for the proper management of these wetlands. Therefore, this study sought to extract and monitor wetland ecohydrological dynamics (surface water and inundation extent) using monthly Sentinel-2 MSI remotely sensed datasets. These dynamics were assessed for the period between July 2020 and June 2021 using the modified normalised difference water index (MNDWI), normalised difference moisture index (NDMI) and normalised difference phenology index (NDPI) derived from Sentinel-2 MSI data. The results showed that the rainy season (Dec 2020 – Feb 2021) had a larger-water coverage extent (10948 m2 (0.05%) to 31594 m2 (0.13%)) when compared to the dry season (July 2020: 19157 m2 (0.04%) and June 2021:14429 m2 (0.03%)). The extent of surface area declined during the dry period due to less rainfall (0.20 mm) and decreased actual evapotranspiration (9.90 mm – 10.43 mm). Further, the NDPI showed a high concentration of wetland vegetation between the months of October 2020 and April 2021. In contrary, higher moisture content was observed between December 2020 to April 2021. The increase in vegetation concentration and moisture content reflects the spatial extent of inundation extent. Wetland water extent, soil moisture, and vegetation condition were assessed with high overall accuracies, ranging between 70.83% and 97.36%. Overall, the results indicate that small wetlands are characterised with significant variations in levels of inundation and productivity throughout the year.

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Hydrogeochemical and health risk assessment in and around a Ramsar-designated wetland, the Ganges River Basin, India: Implications for natural and human interactions

Khan

Umar

Izhar

2022

Environ Monit Assess

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Modelling the dry season inundation pattern of Yala Swamp in Kenya

Cited by 9 publications

References 24 publications

Spatio-temporal modeling of lake’s ecosystem and dynamism in response to changing environment. A case study of L. Olbolossat in Kenya

Spatio-temporal modeling of lake’s ecosystem and dynamism in response to changing environment. A case study of L. Olbolossat in Kenya

An Assessment of Small Wetland Ecohydrological Dynamics Using Sentinel-2 MSI Derived Spectral Indices in Semi-Arid Environments of South Africa

Hydrogeochemical and health risk assessment in and around a Ramsar-designated wetland, the Ganges River Basin, India: Implications for natural and human interactions

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