Quantification of Soil Losses along the Coastal Protected Areas in Kenya

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.

RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection in Fincha Catchment, Abay River Basin, Ethiopia.

“…The slope length and steepness (LS) is also another factor that describes the sensitivity of soil to erosion that governs the velocity of runoff water exerting high pushing forces on the soil particles and causing detachment of soil particles, which in turn lead to erosion. In this catchment, the slope ranges from 0 to 79.9 %, and due to the steepness of the slope, the soil loss is very visible especially for the slope values of more than 11% [6]. The ranges of slope in degree (Fig.…”

“…The annual dramatic increment of the depletion of very important soil nutrients (fertility) exposes the residents of this catchment to high expenses of money [5] to use artificial fertilizers to increase the yield. The intrusion of runoff from eroded soil into a water source [6] invites harmful pollutants and chemicals to change both the physical and chemical properties of sources of water. Even if soil erosion is a globally happening natural hazard [7] in the world, its effects are very serious in developing countries as a result of the inability of restorative lost soil and valuable nutrients.…”

Water Erosion Risk Assessment in the Kenya Great Rift Valley Region

“…Some of the main hydrological models include the Universal Soil Loss Equation (USLE) [27], and its revised version (Revised Universal Soil Loss Equation (RUSLE)), Agricultural Non-Point Source Model (AGNPS), Morgan-Morgan-Finney (MMF), Soil and Water Assessment Tool (SWAT), Water Erosion Prediction Project (WEPP), Erosion Productivity Impact Calculator (EPIC), European Soil Erosion Model (EUROSEM), and The Limberg soil erosion mode (LISEM) [26]. These are broadly classified into physical and experiential models [28]. The applicability of a particular model generally depends on watershed spatial scale or characteristics, data accessibility and efficiency.…”

“…In the USLE study by Mati et al [24], 36% of the Upper Ewaso Ng'iro basin was predicted to suffer from mean erosion rates above the tolerable rate of 10 t ha −1 yr −1 mostly in the overgrazed rangelands. However, despite the presence of soil erosion in the physiographical regions of Kenya, few studies have applied the RUSLE model for spatial temporal evaluations particularly at the regional or national level [25,28,31,36]. The present study targets the GRV region of Kenya which covers about 33% of the country's total surface area with an aim to quantify (i) estimate the magnitude of potential soil loss rates in 1990 and 2015; (ii) assess the spatial changes among soil erosion risk classes between the two periods; (iii) identify priority areas for SWC; and (iv) quantify annual soil loss rates in Kenya Great Lakes ASAL basins, topography and protected areas.…”

RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection in Fincha Catchment, Abay River Basin, Ethiopia.

“…The annual dramatic increment of the depletion of very important soil nutrients (fertility) exposes the residents of this catchment to high expenses of money (Panagos et al, 2018) to use artificial fertilizers to increase the yield. The intrusion of runoff from eroded soil into a water source (Hategekimana et al, 2020) invites harmful pollutants and chemicals to change both the physical and chemical properties of sources of water. Even if soil erosion is a globally happening natural hazard (Markose & Jayappa, 2016) in the world, its effects are very serious in developing countries as a result of the inability of restorative lost soil and valuable nutrients.…”