A Climate-Smart Approach to the Implementation of Land Degradation Neutrality within a Water Catchment Area in Kenya

Abstract

At the sub-national level, the United Nations Convention to Combat Desertification (UNCCD) proposes the analysis and contextualization of land degradation-neutrality (LDN) at a water catchment scale to provide decision support for the formulation of policies and programmes towards transformative LDN interventions. Building on a number of national LDN studies in Kenya, an approach for the implementation of LDN that is based on the spatial and temporal characterization of key land degradation and climate change variables was defined. For a selected water catchment area, the LDN baseline was computed, the drivers that affect land degradation and regeneration trends within the main land cover types were identified and described, the trends of key climate change variables were described, and appropriate sustainable land management interventions for the main land cover types were identified. A climate-smart landscape approach that delineated the catchment area into zones focused on adaptation, and both adaptation and mitigation objectives was then proposed. The operationalization of a climate-smart landscape will require significant investment to not only provide an understanding of the bio-physical processes and interactions occurring at the catchment level but also to develop the institutional and technical capacities of relevant actors. The landscape approach proposed for the catchment area has the potential to improve livelihoods and the productivity of ecosystems while concurrently facilitating synergies between land degradation, climate change, and other development objectives.

Assessing the Underlying Drivers of Change over Two Decades of Land Use and Land Cover Dynamics along the Standard Gauge Railway Corridor, Kenya

“…Grassland decreased significantly in the lower midland zone, and most of it was replaced by bare land which increased substantially in the same zone. Grassland decreases could be due to land degradation with much of this zone being drylands that are more vulnerable to high surface runoff during the rainy season as well as prolonged, severe droughts during the dry season [60][61][62][63][64]. This agrees with the International Reference and Information Centre (ISRIC) report on global assessment of land degradation which indicated that the potential areas of land degradation were the drylands which include the Eastern region of Kenya [61].…”

“…Urbanisation has been spurred by the devolution of resources over the past two decades, starting with the establishment of the Constituency Development Fund in 2003 [86] and later in 2013 by the implementation of the devolved system of governance, which resulted in greater socio-economic development in the counties [87], more employment opportunities [86], and construction of more buildings (e.g., schools and health facilities) and roads and railways [86,88]. Other related factors include the subdivision of agricultural land and group ranches into small units for settlement, driven by high urban population growth rate, inefficient land use policies [63,64,89] and infrastructure development [6]. We found that GDP contributes highly to a growing proportion of the population living in urban centres, as indicated also by [90].…”

Quantifying the Environmental Impact of Standard Gauge Railway (SGR) on Land Cover Changes along the Nairobi-Kiambu Corridor from 2016 to 2019

“…The flooding may be indicative of the loss in vegetation cover. This will be exacerbated by the expected increase in precipitation extremes due to landscape stressors on natural vegetation leading to negative feedback as reported by [30]. Similar reports of flooding along the SGR have been recorded.…”

A Remotely Sensed Study of the Impact of Meteorological Parameters on Vegetation for the Eastern Basins of Afghanistan

“…Since 2014, Kenya's National Drought Management Authority (NDMA) uses the vegetation condition index (VCI) as the basis for providing disaster contingency funds to counties in drought conditions [50]. VCI is a normalized pixel-based NDVI to separate long-term ecosystem changes from short-term climate-related NDVI fluctuations and to reflect relative changes in vegetation conditions from very poor to optimal [51,52].…”