Wang, Jingyu

The heavy global demand for sand in various sectors of the economy subjects the Vietnamese Mekong Delta to correspondingly high amounts of sand mining—a process that started in the early 1990s contributing significantly to the Vietnamese economy. The impacts of intensive sand mining and mining-related industries damage the integrity of the river and local communities. Much of the literature focuses on the former, exposing people to the deleterious implications of sand mining on the physical environment. This study aims to fill the gap on the less explored latter through the lenses of place and landscape per human geography tradition, using qualitative methods of thirty-five interviews with locals, video recordings, and sound measurements to highlight the impacts of sand mining and mining-related industries. This study revealed that sand mining and its associated activities are responsible for people’s perceptions of notable air, land, and noise pollution, as well as substantial harm to the urban environment. Over 80 percent of interviewed locals acknowledged the disruptions to their daily lives and a substantial loss of their sense of place. These findings shed light on narratives frequently overlooked by policymakers, emphasizing the urgency of addressing these issues for a sustainable future.

Global sand demand due to infrastructure construction has intensified sand mining activities in many rivers, with current rates of sand extraction exceeding natural replenishment. This has created many environmental problems, particularly concerning riverbank stability, which adversely affects the livelihoods of people in the Vietnamese Mekong Delta (VMD). However, sand mining’s social impacts in the region remain inadequately understood. Here we assess locals’ perception of sand mining activities in the VMD and its impacts on riverbank erosion. Residents living along the Bassac River, a hotspot of sand mining, were interviewed. Our results showed that while sand mining is perceived as destructive to the environment, few were aware of its role in worsening riverbank erosion. Only residents directly affected by riverbank collapse were aware of the implications of sand mining and its negative effect on bank stability, as they seem to have actively sought clarification. Our findings highlight the need for greater awareness and understanding among the locals regarding sand mining’s impact on riverbank stability.

Hail and tornadoes are hazardous weather events responsible for significant property damage and economic loss worldwide. The most devastating occurrences of hail and tornadoes are commonly produced by supercells in the United States. However, these supercells may also grow upscale into mesoscale convective systems (MCSs) or be embedded within them. The relationship between hail and tornado occurrences with MCSs in the long-term climatology has not been thoroughly examined. In this study, radar features associated with MCSs are extracted from a 14-year MCS tracking database across the contiguous United States, and hazard reports are mapped to these MCS features. We investigate the characteristics of hail and tornadoes in relation to MCSs, considering seasonal and regional variabilities. On average, 8 %–17 % of hail events and 17 %–32 % of tornado events are associated with MCSs, depending on the criteria used to define MCSs. The highest total and MCS-associated hazard events occur from March to May, while the highest MCS-associated portion (23 % for hail and 45 % for tornadoes) is observed in winter (December–February) due to the dominance of MCSs caused by strong synoptic forcing. As hailstone size increases, the fraction associated with MCS decreases, but there is an increasing trend for tornado severity from EF0 to EF3 (Enhanced Fujita Scale). Violent tornadoes at EF4 and EF5 associated with MCSs were also observed, which are generated by supercells embedded within MCSs.

Reliable Subseasonal-to-Seasonal (S2S) forecasts of precipitation are critical for disaster prevention and mitigation. In this study, an innovative hybrid method CSG-UNET combining the UNET with the censored and shifted gamma distribution based ensemble model output statistic (CSG-EMOS), is proposed to calibrate the ensemble precipitation forecasts from ECMWF over the China mainland during boreal summer. Additional atmospheric variable forecasts and the data augmentation are also included to deal with the potential issues of low signal-to-noise ratio and relatively small sample sizes in traditional S2S precipitation forecast correction. The hybrid CSG-UNET exhibits a notable advantage over both individual UNET and CSG-EMOS in improving ensemble precipitation forecasts, simultaneously improving the forecast skills for lead times of 1–2 weeks and further extending the effective forecast timeliness to ∼4 weeks. Specifically, the climatology-based Brier Skill Scores are improved by ∼0.4 for the extreme precipitation forecasts almost throughout the whole timescale compared with the ECMWF. Feature importance analyze towards CSG-EMOS model indicates that the atmospheric factors make great contributions to the prediction skill with the increasing lead times. The CSG-UNET method is promising in subseasonal precipitation forecasts and could be applied to the routine forecast of other atmospheric and ocean phenomena in the future.