Advancing drought understanding and prediction in the Vietnamese Mekong Delta

Zhou, Keke (2025) Advancing drought understanding and prediction in the Vietnamese Mekong Delta. PhD thesis, University of Glasgow.

Abstract

Drought, one of the most destructive climate related natural hazards, affects millions of people worldwide and poses substantial challenges in the Vietnamese Mekong Delta (VMD), one of Southeast Asia’s largest deltas. In recent decades, particularly during 1991 1994, 1998, 2005, 2010, 2015 2016, and 2019 2020, the VMD has suffered from severe and prolonged droughts that resulted in significant socioeconomic impacts. In this delta, droughts often result in severe clean water shortages and extensive damage to cropland. Despite these profound impacts, the mechanisms driving these droughts, including anomalies in the atmospheric moisture transport and land atmosphere (LA) interactions, and the prediction of droughts in the VMD remain underexplored. Addressing these gaps is crucial for enhancing drought preparedness and developing effective drought mitigation strategies.

Accordingly, this thesis aims to achieve three primary objectives : 1) to elucidate the sources of precipitation moisture and identify the dominant factors influencing these sources during drought periods in the VMD ; 2) to quantitatively assess the LA interactions in the VMD using advanced deep learning techniques; 3) to develop an accurate deep learning model capable of predicting droughts in the VMD on account of atmospheric conditions from the external precipitation source region. The first two objectives are designed to deepen understanding of the mechanisms and processes driving droughts in the VMD, while the third aims to utilize these insights to provide accurate drought predictions.

To better understand the process es of atmospheric moisture transport, the Water Accounting Model-2layers (WAM-2layers), an Eulerian based moisture tracking model, was employed to identify the primary moisture sources of precipitation in the VMD from 1980 to 2020. In addition, for the first time, the causal inference algorithms were introduced to analyze the causal relationships among variables involved in moisture transport, specifically, to identify which factor drives the moisture transport process and dominates the amount of tracked moisture. The analysis revealed that (1) over 60% of precipitation in the VMD originates from external moisture sources (60.4 93. 3%), with local recycling contributing from 1.2 % to 27.1%; (2) seasonal shifts in monsoon patterns strongly influence the origins of moisture: during the dry season, the South China Sea (northeast) serves as the dominant source, while the Bay of Bengal (southwest) becomes the primary origin during the wet season; (3) based on the causal inference algorithms, atmospheric humidity and wind speed in the upwind area were identified as the principal factors influencing moisture transport during dry and wet seasons, respectively; (4) large scale forcings (e.g., El Niño and La Niña) were found to affect the processes of affect the processes of moisture transport significantly and these effects vary spatially and seasonally across the VMD’s precipitationshed; (5) local atmospheric conditions, including atmospheric instability (e.g., convective available potential energy, CAPE) and local atmospheric humidity, also play a crucial role in modulating moisture recycling efficiency.

As for the interactions among LA variables, the Long- and Short-term Time-series Network (LSTNet) was applied to model these dynamics over the VMD. The key findings are as as followsfollows: (1) the LSTNet model demonstrated superior performance compared to the traditional regional climate model in simulating key variables (i.e., precipitation, soil precipitation, soil moisture, sensiblemoisture, sensible and and latent heat) during both dry and wet seasons. It exhibited higher accuracy and lower bias, underscoring its suitability for modeling LA interactions in the VMD; (2) this deep learning model effectively captured the relative importance of key variables within the LA interactions, highlighting the critical roles of soil moisture and sensible heat, particularly during dry periods when their negative anomalies substantially reduce precipitation. For example, anomalies in sensible heat were found to decrease precipitation by up to 20% during dry periods, primarily through interactions with temperature and convective inhibition (CIN). Similarly, soil moisture strongly influences precipitation in both dry and wet periods, with deficits leading to reductions in precipitation of up to 30%; (3) projected declines in soil moisture coupled with increases in sensible heat are expected to exacerbate precipitation deficits under changing climatic conditions. By 2075-2099, a 10% increase in sensible heat could reduce precipitation by 3.76% in dry seasons.

Finally, exploring the utility of atmospheric conditions from external precipitation source regions, the deep neural network, Convolutional Gated Recurrent Unit (ConvGRU) was developed to enhance accuracy in drought prediction. The ConvGRU model exhibited exceptional performance in predicting drought conditions at a 3-month lead time, which successfully predicts approximately 90% of meteorological drought events and about 80% of agagricultural drought events, with ricultural drought events, with fewer than 10% false predictions for drought months and events. Furthermore, ConvGRU predicts about 70% and 80% compound dry-hot months and events, respectively. The outstanding performance of the ConvGRU model in drought prediction at the 3-month lead time largely attributed to the delayed impacts of external atmospheric conditions, including specific humidity and U- and V-wind, on the VMD’s drought conditions through the water vapor transport process. Incorporating the atmospheric data from these external precipitation source regions significantly improvess the ConvGRU model’s ’s predictive capability, particularly at the lead time of 3 months.

In summary, this research not only advances the understanding of mechanisms driving drought dynamics including external atmospheric moisture transport and local LA interactions, but also establishes an innovative, effective model for drought prediction. These research developments are vital for improving drought resilience and adaptability in the VMD, and offering substantial benefits for regional drought management strategies.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: Supported by funding from a PhD scholarship from the College of Social Sciences.
• Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
• Colleges/Schools: College of Social Sciences > School of Social & Environmental Sustainability
• Supervisor's Name: Shi, Dr. John Xiaogang and Renaud, Professor Fabrice
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-85210
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 18 Jun 2025 13:28
• Last Modified: 18 Jun 2025 13:34
• Thesis DOI: 10.5525/gla.thesis.85210
• URI: https://theses.gla.ac.uk/id/eprint/85210

Related URLs

• Article DOI
• Article DOI