Shifting climate zones and expanding tropical and arid climate regions across Kenya (1980–2020)

Abstract

Human-induced climate change significantly alters the spatiotemporal characteristics of climate zones, which drives agricultural land use and ecosystem change. However, the detectability of shifting climate zones and the rate and time of the changes has yet to be adequately addressed at the regional-to-local scale. We mapped and analyzed changes to temperature and precipitation across Kenya during the past four decades, and linked those changes to shifts in the geographic distribution and arrangement of climate zones at regional scales. We observed an approximate 1 °C increase in average annual temperature over the 40-year period. A total of 76,346 km2 shifted from cooler to hotter zones, while 1298 km2 shifted from hotter to cooler zones. Tropical climate regions expanded from 91 to 93%, with over 13,000 km2 shifting from alpine and temperate to tropical regions. Average annual precipitation demonstrated little or no trend, but substantial spatial changes were observed. A total of 136,129 km2 shifted from wetter to drier zones, while 23,317 km2 shifted from drier to wetter zones. Arid climate regions expanded from 72 to 81%, a roughly 50,000 km2 shift from humid and semi-humid-to-semi-arid to arid regions. Overall, there was a 207,557 km2 shift in temperature and precipitation zones. As the climate zones predominately shift toward hotter and drier conditions, climatic diversity will decline, and in turn, ecosystem diversity and the ecosystem goods and services to society will decline. The changes also have broader global implications in terms of their contribution to global drylands as well as influencing earth system cycles. Overall, such information can better inform the Kenyan National Climate Change Response Strategy and be used to reach the UN Sustainable Development Goals.

MERS-Coronavirus across Kenya: a spatial examination of social and environmental drivers

“…Smallholder farmers dominate the livestock sector in Kenya with three main livestock production systems: pastoral; dairying; and ranching (Cecchi et al, 2010). Additionally, agricultural and livelihood practices in Kenya are tightly linked to agro-climatic zones (ACZs), which are the delineation of landscapes into regions with relatively homogeneous and contiguous areas based on similar climate characteristics (Boitt et al 2014; Kogo et al 2021; Lawrence et al, 2023a,b; (Recha, 2019)). Primarily, the ACZs in Kenya represent a temperature gradient from alpine, to temperate, to tropical regions, and a moisture gradient from humid to arid regions (Gikonyo et al, 2018).…”

“…Seldom, the influence of climate and agricultural land use have been examined related to emerging infectious diseases in a spatially explicit way at regional scales. This is especially true for Middle East Respiratory Syndrome coronavirus (MERS-CoV) that is prevalent in dromedary camels across Kenya where climate and agricultural land-use change are conspicuous (Lawrence et al, 2023a,b).…”

Spatial changes to climatic suitability and availability of agropastoral farming systems across Kenya (1980–2020)

“…Climate change is a major threat to Kenyan farming systems with seasonal changes in precipitation and temperature of varying severity and duration that negatively affect crop production and food security to the already vulnerable communities in the arid and semi-arid areas (Kogo et al, 2021;Lawrence et al, 2023). Further, agricultural development and livelihood practices in Kenya are tightly linked to ACZs (Recha, 2019).…”

“…Next, we assessed the distribution (mean and standard deviation) of temperature and precipitation within each respective temperature and moisture zone. We compared the distributions to similar information from the Sombroek et al (1982) ACZ map and developed discrete average ranges of both climate variables within each zone to represent the temperature and precipitation zones (Appendix B: Lawrence et al, 2023).…”

“…We evaluated the assessments using error matrices, an overall assessment score, confidence intervals, and kappa statistic (Appendix D). Finally, and based on the assessment results, we applied the trained support vector machine algorithm to the 2000 and 2020 climate raster images to create classified maps of temperature and precipitation zones in those years (Appendix E: Lawrence et al, 2023). Assessments of the support vector machine algorithm's performance were also conducted on the 2000 and 2020 classified maps in comparison to the 2000 and 2020 climate reference maps.…”