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Dynamics of oases in drylands of East Asia in the 21st century: Drivers, impacts, and future projections
Ms ZHU Huixin
( Supervisor: Prof Jimmy Li )
Abstract:
Oases are the highlights of arid and semi-arid ecosystems. Arid and semiarid areas account for more than 60% of the land area in East Asia, which experiences severe drought periodically. Among the two primary Eastern Asia countries, arid and semi-arid areas account for more than 60% of land in China and more than 70% of land in Mongolia. In these water-scarce countries, oases have historically played significant economic and cultural roles. However, oases are highly dynamic and vulnerable to climate change and human activities. These pressures result in significant changes over time in the composition, extent, and spatial distribution of oases.
Nowadays, due to the complex natural geography, diverse climate zones, and different national environmental protection policies in various countries in East Asia, the distribution and dynamic changes of oases in drylands of East Asia and the response of the entire region to environmental changes in the 21st century are still unclear. Although previous research has made contributions to oasis mapping in different regions, most of them used existing finished product data to conduct small-scale oasis research. Research on the spatiotemporal evolution of regional oasis in East Asia and the internal development mechanism of the system is still blank. On the other hand, a variety of advanced machine learning technologies provide more accurate possibilities for extracting and classifying oasis ecosystem information, and also provide a more powerful framework for simulating the dynamic evolution of oasis.
The objective of this study is to establish a dataset of oases distribution in drylands of East Asia from 2000 to 2020, depict the spatiotemporal evolution of oases in drylands of East Asia, and understand the interaction between their ecological dynamics and external driving factors such as climate change and human intervention, and evaluate impact of oasis landscape changes on local and regional environmental conditions and socioeconomic factors. We will use a variety of machine learning technologies in the GEE platform to identify and classify oases in drylands of East Asia since the 21st century and optimize oasis mapping methods. A random forest model will be used to identify key drivers of oasis evolution and quantify their contributions and impacts. Establish structural equations models, identify key nodes in oasis evolution, and use the MCA method to analyze and evaluate the impact of oasis ecosystems on water resources, land use patterns, carbon cycles, and human livelihoods. Another objective is to employ predictive models, particularly the CA-Markov model, to predict future land use changes under different climate scenarios and socioeconomic conditions.
This research aims to provide a comprehensive framework for understanding the complex dynamics of oases in drylands of East Asia. The findings will provide practical insights to policymakers and researchers in land use planning and conservation in arid areas and provide scientific references for the sustainable development of oasis ecosystems in this region and other large areas with limited water resources.
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