Impact of climate change on meteorological, hydrological and agricultural droughts in the Lower Mekong River Basin: a case study of the Srepok Basin, Vietnam

peer reviewedThe objective of this study is to assess future changes in meteorological, hydrology and agricultural droughts under the impact of changing climate in the Srepok River Basin, a subbasin of LMB, using three drought indices; standardized precipitation index (SPI), standardized runoff index (SRI) and standardized soil moisture index (SSWI). The well-calibrated Soil and Water Assessment Tool (SWAT) is used as a simulation tool to estimate the features of meteorological, hydrological and agricultural droughts. The climate data for the 2016–2040 period is obtained from four different regional climate models; HadGEM3-RA, SNU-MM5, RegCM4 and YSU-RSM, which are downscaled from the HadGEM2-AO GCM. The results show that the severity, duration and frequency of droughts are predicted to increase in the near future for this region. Moreover, the meteorological drought is less sensitive to climate change than the hydrological and agricultural droughts; however, it has a stronger correlation with the hydrological and agricultural droughts as the accumulation period is increased. These findings may be useful for water resources management and future planning for mitigation and adaptation to the climate change impact in the Srepok River Basin

SURVEY ON VOCABULARY LEARNING STRATEGIES OF HIGH–QUALITY ENGLISH STUDIES PROGRAM STUDENTS, SCHOOL OF FOREIGN LANGUAGES, CAN THO UNIVERSITY, VIETNAM

This research study aimed to investigate the usage of vocabulary learning strategies among English Studies students at Can Tho University (CTU), specifically those under the high-quality program at the School of Foreign Languages (SFL). The primary objective of the study was to identify the most commonly used strategies for learning English vocabulary and to compare the similarities and differences in how these strategies were applied among students by academic year. A total of 200 survey responses from SFL, CTU got involved in the study, and 12 of whom joined a semi-structured interview. The data gathered were analyzed using both descriptive and inferential statistical techniques. The results of this study provided insights into effective vocabulary learning strategies and would facilitate the improvement of English language teaching and learning practices at the university level.  Article visualizations

INVESTIGATING THE EXPERIENCES OF STUDENTS WITH DISABILITIES WITH E-LEARNING DURING THE COVID-19 PANDEMIC IN VIETNAMESE HIGHER EDUCATION

This study uses a mixed-methods approach to investigate the experiences of Vietnamese university students with disabilities (visual/mobility impairments) with e-learning as a consequence of emergency remote teaching during the COVID-19 pandemic. An analysis of the ideas of 20 surveyed students with disabilities at eight universities in Ho Chi Minh City and six students interviewed afterward shows that students can change their study habits to adapt to e-learning and to enjoy this model of learning. However, the participants revealed that they also want to experience face-to-face learning so that they can interact with their lecturers and peers more effectively and in more diverse ways, as well as assimilate lectures more easily. Furthermore, the research shows that various adjustments should be made by system designers, universities, and lecturers to make e-learning friendlier to disabled students. The recommended adjustments include designing easy-to-use learning tools and platforms, providing lecturers with the necessary tools and facilities to design lessons appropriate for all students, providing psychological and technical support for disabled students, choosing user-friendly learning applications and platforms, providing students with suitable learning resources, and modifying testing and assessment methods

Influence of foliar application with Moringa oleifera residue fertilizer on growth, and yield quality of leafy vegetables

Biofertilizers produced from organic materials help to promote the growth, and yield quality of crops and is more environmentally friendly than chemical fertilizers. Moringa oleifera is a leafy vegetable whose leaves are also used to make biofertilizers. The use of moringa non-edible parts in biofertilizer preparation remains under-explored. In this study, a procedure to produce moringa foliar biofertilizer (MFB) from non-edible parts was developed. The effect of composting time (3 to 4 months) on the quality of MFB was investigated, and four-month incubation was found suitable for biofertilizers yield with the highest nitrogen content and optimal pH. Furthermore, the influences of MFB doses (20 to 100 mL per Litre) on the growth of lettuce and mustard spinach were studied. The yield of these leafy vegetables was the highest at 100 mL per Litre of MFB spray. Finally, MFB was compared with other commercial foliar sprays, including chitosan fertilizer and seaweed fertilizer. Each foliar treatment was applied every five days until five days before harvest. Plant height, the number of leaves, canopy diameter, leaf area index, actual yield, ascorbic acid content, and Brix were found to be similar in lettuce sprayed with MFB, chitosan, and seaweed fertilizers. In conclusion, the application of MFB promoted the growth and yield of mustard spinach

Corrosion of stainless steel water storage tanks exposed in coastal atmospheric conditions

Results of corrosion survey for stainless steel tanks used in water storage at various coastal areas are presented. Corrosion damages were revealed at both the outer and inner surfaces of tanks made of 304 and 201 steel grades. Corrosion deterioration was more severely observed for the atmospheric areas with higher airborne salinity and time of wetness.  Corrosion products examined by visual inspection and SEM-EDX technique show relatively distinctive characteristics for outer and inner surfaces which are attributed to different mechanisms of corrosion initiated by various corrosive agents in the atmosphere. Atmospheric chlorides from airborne sources are considered the main reason for causing corrosion of 304 and 201 steel grade water tanks

Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts

As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions and feedbacks in complex human-water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e., two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed, and in the quantity of socio-hydrological data. The benchmark dataset comprises: 1) detailed review style reports about the events and key processes between the two events of a pair; 2) the key data table containing variables that assess the indicators which characterise management shortcomings, hazard, exposure, vulnerability and impacts of all events; 3) a table of the indicators-of-change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators-of-change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses e.g. focused on causal links between risk management, changes in hazard, exposure and vulnerability and flood or drought impacts. The data can also be used for the development, calibration and validation of socio-hydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al. 2023, link for review: https://dataservices.gfz-potsdam.de/panmetaworks/review/923c14519deb04f83815ce108b48dd2581d57b90ce069bec9c948361028b8c85/).</p

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Evaluation of five gridded rainfall datasets in simulating streamflow in the upper Dong Nai river basin, Vietnam

Rainfall data with an appropriate spatial resolution is a key input to hydrological models. However, networks of rain gauges are often sparsely and unevenly distributed in large catchments, especially in developing countries. High-resolution rainfall datasets, such as the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the Climate Forecast System Reanalysis (CFSR), the Climatic Research Unit Time Series (CRU-TS), the Global Precipitation Climatology Centre (GPCC) and the Tropical Rainfall Measuring Mission (TRMM), have become available to overcome such limitations. The objective of this study was to evaluate the impacts of four land-based rainfall products (APHRODITE, CFSR, CRU-TS, and GPCC) and a satellite-based rainfall product (TRMM) on streamflow of the upper catchment of Tri An reservoir in Vietnam using the Hydrological Modeling System (HEC-HMS). In addition, the available rain gauges data were used for comparison purpose. Result indicates that the TRMM and GPCC data show their best match to rain gauges data in simulating the streamflow in the period 1999–2007. Generally, the results indicate that the TRMM and GPCC data could be alternative solutions

Impact of Climate Change on Precipitation Extremes over Ho Chi Minh City, Vietnam

In the context of climate change, the impact of hydro-meteorological extremes, such as floods and droughts, has become one of the most severe issues for the governors of mega-cities. The main purpose of this study is to assess the spatiotemporal changes in extreme precipitation indices over Ho Chi Minh City, Vietnam, between the near (2021–2050) and intermediate (2051–2080) future periods with respect to the baseline period (1980–2009). The historical extreme indices were calculated through observed daily rainfall data at 11 selected meteorological stations across the study area. The future extreme indices were projected based on a stochastic weather generator, the Long Ashton Research Station Weather Generator (LARS-WG), which incorporates climate projections from the Coupled Model Intercomparison Project 5 (CMIP5) ensemble. Eight extreme precipitation indices, such as the consecutive dry days (CDDs), consecutive wet days (CWDs), number of very heavy precipitation days (R20mm), number of extremely heavy precipitation days (R25mm), maximum 1 d precipitation amount (RX1day), maximum 5 d precipitation amount (RX5day), very wet days (R95p), and simple daily intensity index (SDII) were selected to evaluate the multi-model ensemble mean changes of extreme indices in terms of intensity, duration, and frequency. The statistical significance, stability, and averaged magnitude of trends in these changes, thereby, were computed by the Mann-Kendall statistical techniques and Sen’s estimator, and applied to each extreme index. The results indicated a general increasing trend in most extreme indices for the future periods. In comparison with the near future period (2021–2050), the extreme intensity and frequency indices in the intermediate future period (2051–2080) present more statistically significant trends and higher growing rates. Furthermore, an increase in most extreme indices mainly occurs in some parts of the central and southern regions, while a decrease in those indices is often projected in the north of the study area

Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level of the existing surface water. This case study aims to evaluate the performance of twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient boosting, histogram-based gradient boosting, light gradient boosting, and extreme gradient boosting), three decision tree-based algorithms (decision tree, extra trees, and random forest), and four ANN-based algorithms (multilayer perceptron, radial basis function, deep feed-forward neural network, and convolutional neural network), in estimating the surface water quality of the La Buong River in Vietnam. Water quality data at four monitoring stations alongside the La Buong River for the period 2010&ndash;2017 were utilized to calculate the water quality index (WQI). Prediction performance of the ML models was evaluated by using two efficiency statistics (i.e., R2 and RMSE). The results indicated that all twelve ML models have good performance in predicting the WQI but that extreme gradient boosting (XGBoost) has the best performance with the highest accuracy (R2 = 0.989 and RMSE = 0.107). The findings strengthen the argument that ML models, especially XGBoost, may be employed for WQI prediction with a high level of accuracy, which will further improve water quality management