NEXUS OF URBANIZATION, LAND SURFACE TEMPERATURE AND CLIMATE VARIABILITY: - A GEOSPATIAL STUDY THE CASE OF MEKELLE CITY, NORTHERN ETHIOPIA

Abstract

Urbanization and climate variability are complexly intertwined processes, exerting profound, mutual influences and far-reaching effect on socio-ecological systems, particularly in rapidly growing cities. This dynamic relationship reshapes livelihoods, transforms cultural norms, and reconfigures societal behaviors, with consequences that extend beyond city boundaries into adjacent rural environs. When urbanization managed strategically can serve as a substance for sustainable development. However, in the absence of effective planning and regulation, it frequently gives rise to a host of challenges, including abandoned urban sprawl, environmental degradation, pollution, depletion of natural resources, intensified urban heat island (UHI) effects, and exacerbated climate variability. Ethiopia provides a compelling case study of these processes: despite it is characterized by a relatively low urbanization rate but rapid urban growth it’s relatively. Cities such as Mekelle exemplify this trend, marked by a rapid expansion of impervious surfaces, profound land use transformations, and increasingly severe urban thermal stress. This study aimed to analyze urbanization induces spatio-temporal dynamics of urban landscape and its impact on LST, and local climate variability. A multidimensional methodological approach including image enhancement, classification, spectral index analysis, spatio-statistical comparison and advanced machine learning methods were used to identify, interpret, and analyze the recent past-present-and near future patterns and scenario of urban land use dynamics, impacts on urban LST, vulnerability to effects of UHI, and climate variables. The findings reveal that, urban growth has expanded nearly nine fold, increasing from 3,524 hectares in 1980s to an estimated 19,622 hectares by 2020. Furthermore, the study also demonstrated the efficiency of spectral indices in providing a more automated and efficient way for mapping urban land use dynamics and analyzing the trends of urban expansion by enhancing classification accuracy and reducing bias using a conventional supervised approach. The study's findings also indicated that highly intensified urbanization, characterized by increased built-up areas and impervious surfaces, has led to higher LST and expanding UHI zones, especially during February, March, April, & May. Furthermore the impervious urban land surface, built-up, and dry bare soil areas are highly contribute and influence variation on the intensity and caused for the formation of UHI. By integrated a comprehensive set of indicators across three key domains of urban morphology, climatological, and demographic factors, the level of risk and vulnerability to UHI is compared using two distinct vulnerability assessment techniques such as Principal Component Explanatory Factor Analysis (PC-EFA) and the Intergovernmental Panel on Climate Change urban heat island vulnerability (IPCC-UHIV) method. The findings revealed that UHI effects are most intense in areas characterized by extensive impervious surfaces, low vegetation cover, and degraded lands, which significantly hinder natural cooling mechanisms and exacerbate urban heat stress. Notably, built-up areas and buildings covered with heat-absorbing roofing materials, such as corrugated iron, contribute substantially to increase LST. Despite of some methodological differences, both models effectively identified the area’s most vulnerable to urban heat, and confirming the broader spatial patterns of vulnerability across the city. The IPCC-UHIV model, by contrast, provided a more detailed and comprehensive assessment, with a stronger negative correlation (r = -0.116) compared to the PC-EFA model (r = -0.029) with LST. The PC-EFA model overlooked some key indicators due principal component data dimensionality analysis method; it may lead to distinguished differences in vulnerability classifications. A multilayered approach was also used analyzing the complementary interaction and impacts of xvi | P a g e key urban morphology indicators (urban landscape, vegetation cover, built-up area, urban density, and building height) and climate variables (air temperature, precipitation, LST, relative humidity, surface run-off, downward solar radiation, and evapotranspiration) over a 30-year period (1990–2020). The study concluded that urban morphology exerts a significant complex interaction, and influence on the local climate variables, particularly vegetation consistently emerged as a pivotal factor in moderating climatic variables, and emphasizing its essential role in ecological balance and regulation of climate variability. Conversely, dense urban morphology and high impervious surfaces were linked to reduced solar radiation, hindered airflow, and diminished hydrological regulation, exacerbating ecological stress and thermal discomfort. The forecasts result derived from CA–ANN simulations (2020–2063) suggest continued urban sprawl, agricultural land encroachment, and a marked increase in UTFVI-defined thermal stress zones. The study provides valuable empirical evidence supporting the integration of geospatial technologies and climate-sensitive frameworks in urban policy-making. It emphasizes the urgency of proactive interventions to enhance urban resilience and livability in the face of escalating climate variability and anthropogenic pressures. It also suggested that by embracing data-driven planning tools and fostering community engagement, cities like Mekelle can mitigate environmental degradation, reduce UHI impacts, and visualize a sustainable urban future.