Quantification of Soil Losses along the Coastal Protected Areas in Kenya

Yves Hategekimana, Mona Allam, Qingyan Meng, Yueping Nie and Elhag Mohamed
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Yves Hategekimana: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Mona Allam: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Qingyan Meng: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Yueping Nie: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Elhag Mohamed: Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University Jeddah, Jeddah 21589, Saudi Arabia

Land, 2020, vol. 9, issue 5, 1-16

Abstract: Monitoring of improper soil erosion empowered by water is constantly adding more risk to the natural resource mitigation scenarios, especially in developing countries. The demographical pattern and the rate of growth, in addition to the impairments of the rainfall pattern, are consequently disposed to adverse environmental disturbances. The current research goal is to evaluate soil erosion triggered by water in the coastal area of Kenya on the district level, and also in protected areas. The Revised Universal Soil Loss Equation (RUSLE) model was exercised to estimate the soil loss in the designated study area. RUSLE input parameters were functionally realized in terms of rainfall and runoff erosivity factor (R), soil erodibility factor (K), slope length and gradient factor (LS), land cover management factor (C) and slope factor (P). The realization of RUSLE input parameters was carried out using different dataset sources, including meteorological data, soil/geology maps, the Digital Elevation Model (DEM) and processing of satellite imagery. Out of 26 districts in coastal area, eight districts were projected to have mean annual soil loss rates of >10 t·ha −1 ·y −1 : Kololenli (19.709 t·ha −1 ·y −1 ), Kubo (14.36 t·ha −1 ·y −1 ), Matuga (19.32 t·ha −1 ·y −1 ), Changamwe (26.7 t·ha −1 ·y −1 ), Kisauni (16.23 t·ha −1 ·y −1 ), Likoni (27.9 t·ha −1 ·y −1 ), Mwatate (15.9 t·ha −1 ·y −1 ) and Wundanyi (26.51 t·ha −1 ·y −1 ). Out of 34 protected areas at the coastal areas, only four were projected to have high soil loss estimation rates >10 t·ha −1 ·y −1 : Taita Hills (11.12 t·ha −1 ·y −1 ), Gonja (18.52 t·ha −1 ·y −1 ), Mailuganji (13.75.74 t·ha −1 ·y −1 ), and Shimba Hills (15.06 t·ha −1 ·y −1 ). In order to mitigate soil erosion in Kenya’s coastal areas, it is crucial to regulate the anthropogenic disturbances embedded mainly in deforestation of the timberlands, in addition to the natural deforestation process caused by the wildfires.

Keywords: soil erosion; protected areas (PAs); RUSLE; GIS; coast of Kenya (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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