Physical Landscape and
Environmental Change
physical environment

The state of our physical environment affects our livelihood and well-being. Our common sustainable future depends on how we understand and manage environmental changes that occur in all spatial and temporal time scales. In this research cluster, we engage multidisciplinary initiatives and collaborate with emerging countries to study physical landscape and environmental changes. Using our cutting-edge research outcomes, we explore and provide sustainable solutions to our changing environments. Our research projects aim to:


(1) understand local and global changes in geological past and in human history;

(2) apply the latest field monitoring techniques to evaluate ecological well-being and health risk; and

(3) employ high-performance computing to investigate complex hydroclimatic changes.

PM satellite1

GRF Project (PI: Dr. Meng Gao) Improving predictions of ground-level PM2.5 concentrations in Asia with deep learning and the world’s first geostationary air pollution satellite

Ambient particles raise worldwide concerns due to their impediments on human health, and important roles in the Earth’s weather and climate system via altering radiation and clouds. Particles with diameter less than 2.5 micrometers (PM2.5) are small enough to enter deeply into human lungs, posing the greatest short-term and long-term risks to human health. Accordingly, sources of PM2.5 and particle precursors are highly regulated in most industrialized countries. PM2.5 can linger in the atmosphere for days and exhibit substantial spatiotemporal variations. An accurate depiction of the dynamic evolution of PM2.5 remains a challenge, but urgently needed for better regulation of air quality and health risk assessment. Commonly, ground-based monitoring networks are established to characterize the PM2.5 concentrations in highly populated regions and protected areas such as national parks, but large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of monitoring networks. However, such attempts are hampered under cloudy/hazy conditions or during nighttime. In this study, we aim to overcome the long-standing restriction that surface PM2.5 cannot be constrained with satellite remote sensing under cloudy/hazy conditions or during nighttime. We propose to build a deep learning-based model to fill these observational gaps with data from the newly launched world’s first geostationary air pollution satellite GEMS (Geostationary Environment Monitoring Spectrometer, launched on February 18, 2020 in South Korea). Cloud droplet number retrievals will be also used to constrain below-cloud PM2.5 concentrations, and this approach would provide ground-level PM2.5 concentrations with high spatial resolution and 24-hour temporal coverage. Better constrained spatiotemporal distributions of PM2.5 concentrations will help improve health effects studies, atmospheric emission estimates, and predictions of air quality.

flash floods hk

GRF Project (PI: Dr Jianfeng Li) Temporal evolution of sub-daily precipitation extremes in Hong Kong: Dependency on temperature and implications to flash floods

時間分辨率對極端降水特徵推算有重要影響。氣候暖化下,日尺度極端降水的增加率通常被認為遵循Clausius-Clapeyron (CC)關係,也就是當氣溫上升1 oC,大氣持水能力上升約6.5%。而小時尺度極端降水強度增加得更快,也被稱為超CC(super-CC)關係。然而,小時尺度仍難以準確反映歷時小於1小時的暴雨特徵。在全球暖化下,精細時間尺度(如分鐘到小時)極端降水及山洪的演化特徵(如長期趨勢和變化)是防洪設施設計和城市規劃(例如水塘和公路)的重要參數,尤其是對於香港等山多及人口密集地區。因為缺乏足夠時間長度和空間範圍的高質量日內尺度降水資料,人們對日內尺度極端降水(強度、歷時和總降水量等)和山洪對氣溫上升響應的認識仍較有限。另外,具有高度空間差異性的地形以及複雜的地表水文過程使得山洪的發生和過程具有不確定性。過往研究通常只關注日內尺度極端降水強度與山洪的關係,而較少考慮陸面水文特徵及流域過程的影響。 香港是世界人口第四密集的城市,其已發展地區毗鄰陡峭與崎嶇的地形。山多的地形以及濕潤的亞熱帶季風氣候使得該國際金融中心對山洪非常脆弱。本課題將基於香港超過130年的小時尺度降水記錄及超過30年的5分鐘尺度降水觀測數據,研究日內尺度極端降水事件強度、歷時和總量的時間演化及其與氣溫的關係,並使用耦合的水文大氣模式系統WRF/WRF-Hydro考慮陸面水文過程並模擬日內尺度極端降水變化對山洪的影響。本課題有助於提升人們對氣候暖化下日內尺度極端降水變化以及其對山洪潛在影響的認識。本課題研究結果將對香港及其他城市提升氣候變化應變能力,特別是防洪管理,具有實際意義。

pm 2.5 china

Early Career Scheme Project (PI: Dr Meng Gao) Assimilating surface PM2.5 and ozone measurements to improve health exposure assessment and air quality forecasting in South China



GRF Project (PI: Prof Bernie Owen) Temporal Variations in the Controls of Lacustrine Sedimentation During Continental Rift Evolution: Evidence from the Northern Kenya Rift Valley

This study examines rock and sediment outcrops ranging in age from the Miocene to the present (~15–0 million years) in two areas of the northern Kenya Rift Valley.


This study will examine rock and sediment outcrops ranging in age from the Miocene to the present (~15–0 million years) in two areas of the northern Kenya Rift Valley. These include the arid Suguta Valley and, to the southwest, the eroded Tugen Hills. Both areas contain well-exposed deposits laid down in a variety of terrestrial and aquatic environments. This investigation will focus on those deposits formed in spring systems and fresh to saline lakes. The research will pursue four major lines of investigation: 1) a study of modern hot and cold springs and their deposits in the Suguta Valley; 2) characterising modern sedimentation in permanent and ephemeral lakes in the Suguta Valley, as well as changes in water chemistry between spring and river sources and modern saline lakes of the Suguta; 3) An investigation of past environments and sediments formed in ancient fresh to saline lakes that date back several million years in both the Suguta Valley and the Tugen Hills; and 4) integrating information on ancient and modern lakes in the northern Kenya Rift in order to determine if their are any systematic changes that can be related to rifting and volcanism in an evolving rift valley. The project will explore these major aims through field studies and systematic analyses of water (modern lake waters, rivers, springs and groundwater inflows) and sediment (outcrops, short cores) samples. We will provide field descriptions of sediment sequences and return samples for geochemical, sedimentological and diatom analyses. The results of this work will allow us to characterise the modern environments and to decipher the past depositional settings in which ancient sediments accumulated. In previous studies of the southern Kenya Rift, two members of the research team have detected many changes in the types of sediment that have accumulated at different times. These changes in deposition partly reflect varying past climates, but also mirror changes in the stage of development of the rift valley. This study will allow the research team to explore if their are similar, or different, long-term variations in deposition that may reflect changes in the evolving northern Kenya Rift and its tectonic setting and volcanic environments. We will then combine models from our earlier studies in the southern Kenya Rift with new models from this investigation in order to develop a broader-based understanding of how sedimentation changes with time in an evolving rift system.

streamflow west africa

Chun, K. P. et al, 2021.Identifying drivers of streamflow extremes in West Africa to inform a nonstationary prediction model. Weather and Climate Extremes, 33, [100346]

West Africa exhibits decadal patterns in the behaviour of droughts and floods, creating challenges for effective water resources management. Proposed drivers of prolonged shifts in hydrological extremes include the impacts of land-cover change and climate variability in the region. However, while future land-degradation or land-use are highly unpredictable, recent studies suggest that prolonged periods of high-flows or increasing flood occurrences could be predicted by monitoring sea-surface temperature (SST) anomalies in the different ocean basins. In this study, we thus examine: i) what ocean basins would be the most suitable for future seamless flood-prediction systems; ii) how these ocean basins affect high-flow extremes (hereafter referred as extreme streamflow); and iii) how to integrate such nonstationary information in flood risk modelling. We first use relative importance analysis to identify the main SST drivers modulating hydrological conditions at both interannual and decadal timescales. At interannual timescales, Pacific Niño (ENSO), tropical Indian Ocean (TIO) and eastern Mediterranean (EMED) constitute the main climatic controls of extreme streamflow over West Africa, while the SST variability in the North and tropical Atlantic, as well as decadal variations of TIO and EMED are the main climatic controls at decadal timescales. Using regression analysis, we then suggest that these SST drivers impact hydrological extremes through shifts in the latitudinal location and the strength of the Intertropical Convergence Zone (ITCZ) and the Walker circulation, impacting the West African Monsoon, especially the zonal and meridional atmospheric water budget. Finally, a nonstationary extreme model, with climate information capturing regional circulation patterns, reveals that EMED SST is the best predictor for nonstationary streamflow extremes, particularly across the Sahel. Predictability skill is, however, much higher at the decadal timescale, and over the Senegal than the Niger catchment. This might be due to stronger impacts of land-use (-cover) and/or catchment properties (e.g. the Inner Delta) on the Niger River flow. Overall, a nonstationary framework for floods can also be applied to drought risk assessment, contributing to water regulation plans and hazard prevention, over West Africa and potentially other parts of the world.

biomass grassland

Hossain, M.L., and J. Li, 2021. Biomass partitioning of C3- and C4-dominated grasslands in response to climate variability and climate extremes. Environmental Research Letters,16, 074016

The rising temperature, altering precipitation, and increasing extreme events under climate warming affect the stability and sustainability of grassland ecosystems. The dynamics of above-ground biomass (AGB), below-ground biomass (BGB), and biomass partitioning (BGB:AGB ratio) of grasslands are of fundamental importance to understand their feedback to climate change. In this study, we used grassland productivity data extracted from the Oak Ridge National Laboratory Distributed Active Archive Center, Tennessee, USA, in which the AGB was collected within a 1.0 m × 0.25 m quadrat and the BGB was sampled within the center of the quadrat. Using multiple pairwise tests and Pearson's correlation analysis, we assessed the variations of grassland productivity and examined the response of single-harvest and annual biomass partitioning of C3- and C4-dominated grasslands to the growing-season and annual climatic variability and climate extremes in seven sites belonging to four ecoregions (i.e. cold steppe, humid temperate, humid savanna, and savanna). The results show that the annual and single-harvest BGB:AGB ratio varied significantly across the plant types and ecoregions. Overall, the C3-dominated grasslands exhibited a higher BGB:AGB ratio than that of C4-dominated grasslands. Growing-season temperatures (GSTs) were found to be the key determinants in explaining the single-harvest BGB:AGB ratio rather than growing-season precipitation. For instance, the single-harvest BGB:AGB ratio of C4-dominated grasslands increased, while that of C3-dominated grasslands decreased with elevated GSTs. The growing-season extreme dry climates significantly increased the single-harvest BGB:AGB ratio of C4 plants by a large reduction of AGB, potentially affecting the ecosystem functioning and stability. The C3-dominated grasslands in the cold steppe ecoregion are at great threat of drought-induced stress, as we observed that growing-season extreme dry climates reduced, albeit insignificantly, both the single-harvest AGB and BGB. This study provides key insights into factors influencing the biomass partitioning of C3- and C4-dominated grasslands and has important implications for assessing the grassland functioning and stability under increasing climate extremes.