DISENTANGLING TECTONIC- AND CLIMATE-DRIVEN PROCESSES IN THE EVOLUTION OF QUATERNARY RIVERSCAPES. A FRESH APPROACH INTEGRATING FIELD SURVEY, REMOTE SENSING AND GEOMORPHOMETRY INTO ARTIFICIAL INTELLIGENCE/MACHINE LEARNING

Rivers are geomorphic agents that profoundly influence the Earth's surface by responding to tectonic and climatic forces, while simultaneously shaping the landscape and triggering various geomorphological processes. These interactions transform topography across multiple scales, from watershed boundaries to alluvial plains, resulting in erosional patterns and complex landforms that reflect the interplay of factors such as tectonics, litho-structural frameworks, and climate variability. A comprehensive understanding of these factors is essential for reconstructing past geological processes and forecasting future landscape evolution. This thesis presents the results of a three-year investigation into the riverscapes of the central Al-Hajar Mountains (northern Sultanate of Oman), a region characterised by a unique geodynamic setting and arid climate. The Al-Hajar Mountains serve as an ideal natural laboratory for examining the interactions between climate and tectonic activity. Located along the northeastern margin of the Arabian Plate, this orogenic belt is globally renowned for hosting the Semail Ophiolite, one of the most complete and well-preserved obducted ophiolite sequences. Its emplacement during the Late Cretaceous involved the obduction of oceanic lithosphere and deep-sea sediments onto the autochthonous units, comprising pre-Permian sedimentary and volcanic sequences overlain by Permian to Late Cretaceous carbonate platforms. The doming processes, accompanied by regional forebulging, likely coincided with extensional to transtensional regime during obduction and subsequent collapse phases. Quaternary tectonic activity is limited yet evident in the region, primarily through regional uplift, low seismicity, over-incised wadis, marine terraces, and notches. The research project aims to elucidate the mechanisms driving landscape evolution in such complex geological context, focusing on the roles of tectonic uplift, litho-structural controls, and climatic fluctuations during the Quaternary and pre-Quaternary periods. A multidisciplinary approach - integrating field surveys, remote sensing, geomorphometry, and Deep Learning - has been employed to identify and characterise active and fossil geomorphological processes across various scales. This methodology highlights the close links between surface processes, tectonics, and climatic changes, emphasising that landscape evolution is ongoing. The multidisciplinary approach has been applied to various parts of the Al-Hajar region, utilizing different combinations of methods. Beginning with geomorphological mapping that integrates remote sensing techniques and field surveys, this approach highlights the close relationship between surface processes in both mountainous and plain areas and underlying tectonic, litho-structural, and climatic factors, spanning Quaternary and pre-Quaternary periods. The integration of deep learning and geomorphometric techniques provides quantitative tools to quantify the influence of controlling factors and assess how shifts in riverine regimes impact landscape dynamics. Specifically, geomorphometry enables the detection of disequilibrium in landscape regions, revealing that the current landscape remains in a state of evolution, indicative of ongoing geodynamic processes. Concurrently, Deep Learning enhances the detection and classification of fluvial landforms in high-resolution satellite imagery, shedding light on past hydrological conditions and illustrating the combined effects of climate and tectonics on alluvial fan development at mountain foothills. Therefore, the thesis is organised into 3 case studies, each applying one aspect of the multidisciplinary methodology to reconstruct landscape evolution of the central region of the Jebel Akhdar dome and its margins. Chapter 2 features a detailed geomorphological map of Jebel Akhdar dome (JAK), a prominent carbonate dome formed during the obduction of the Semail Ophiolite. This chapter identifies key landforms shaped by complex interactions between tectonics, surface processes, and climate. The region's landscape reflects a long evolutionary history beginning with Late Cretaceous tectonic uplift and doming, followed by Miocene-Quaternary erosion, karstification, and fluvial and gravitational processes. Tectonic activity has driven the development of structural features such as folds, faults, and monoclinal flanks, while karst processes have created extensive underground networks and surface landforms. Fluvial and gravitative processes have further produced incised valleys, alluvial fans, and pediments, with ongoing Quaternary tectonic activity influencing landscape dynamics. Chapter 3 combines morphometric analysis and field surveys to examine the central Al-Hajar Mountains, with a focus on JAK. Field evidence underscores the roles of fluvial networks, structural discontinuities, and karst systems in shaping the current topography. Morphometric data reveal significant spatial variability among tectonic units, reflecting differential erosional patterns. Correlations between morphometric indices and fault density point to spatial variations in the structural control over surface processes. The morphometric patterns observed in the northern and eastern flanks of JAK, as well as in the adjacent Semail Ophiolite and western Hawasina nappes, indicate ongoing erosion likely driven by regional crustal uplift associated with slab stagnation. In contrast, the southern flank of JAK shows comparatively lower erosion rates and well-preserved morphostructures. This asymmetry reflects the long-term geomorphic imprint of doming, where uplift and tilting continue to affect the southern sector, maintaining it in a relatively less eroded state. Chapter 4 develops Deep Learning algorithms for the automated detection of fluvial features to generate a high-resolution geomorphological map of the Barzamani alluvial fan, located at the southern margin of the Al-Hajar Mountains. The region’s extensive alluvial fans, forming coalescing bajada and characterised by minimal vegetation cover due to the semi-arid to arid climate, allow the observation of intricate paleo-drainage systems spanning from the Miocene to Pleistocene. Manual mapping is labor-intensive and prone to subjectivity, motivating the adoption of Machine Learning techniques. A U-Net-based Deep Learning pipeline was implemented on multispectral SPOT 6 high-resolution satellite imagery to produce probabilistic maps of palaeochannel systems - differentiating between paleochannels and the underlying substrate - enabling improved detection of the past hydrological regimes, thus the understanding of landscape evolution driven by climatic and tectonic influences. By integrating geomorphological mapping, geomorphometry, and Deep Learning, this thesis offers a comprehensive understanding of the complex interactions between tectonics, litho-structural setting, and climate, allowing for the quantification of the role of both regional tectonic processes and local structural settings in shaping the landscapes of the northern Sultanate of Oman. The findings contribute to broader insights into tectonic processes, landscape resilience, and future evolution in low to moderate tectonically active, arid environments.