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In this study, the spatiotemporal variability of trends in extreme precipitation events in Ho Chi Minh City during the period 1980–2017 was analyzed based on several core extreme precipitation indices (Rx1day, Rx5day, CDD, CWD, R20mm, R25mm, R95p, and SDII). The non-parametric Mann–Kendall and Sen’s slope methods were used to compute the statistical strength, stability, and magnitude of trends in annual rainfall, as well as the extreme precipitation indices. We found that 64% of the stations had statistically significant upward trends in yearly rainfall, with high magnitudes frequently observed in the northern and southern regions of the city. For the extreme precipitation indices, only SDII and R25mm showed dominantly significant trends. Additionally, there were increasing trends in the frequency and duration at the southern and central regions of the city during the study period. Furthermore, El Niño-Southern Oscillation and Pacific Decadal Oscillation positively correlated with the duration and negatively correlated with the intensity and frequency of extreme precipitation. Thus, water management plans should be adjusted appropriately to reduce the severe impacts of precipitation extremes on communities and ecosystems.

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 2010–2017 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.

The precise rainfall estimate with appropriate spatial and temporal resolutions is a key input to distributed hydrological models. However, networks of rain gauges are often sparsely distributed in developing countries. To overcome such limitations, this study used some of the existing gridded rainfall products to simulate streamflow. Four gridded rainfall products, including APHRODITE, CFSR, PERSIANN, and TRMM, were used as input to the SWAT distributed hydrological model in order to simulate streamflow over the Srepok River Catchment in Vietnam. Besides that, the available rain gauges data were also used for comparison. Amongst the four different datasets, the TRMM and APHRODITE data show their best match to rain gauges data in simulating the daily and monthly streamflow with satisfactory precision in the 2000-2006 period. The result indicates that the TRMM and APHRODITE data have potential applications in driving hydrological model and water resources management in data-scarce and ungauged areas in Vietnam.

The large river basins throughout the world have undergone land-use/land-cover (LULC)-induced changes in river discharge and sediment load. Evaluating these changes is consequently important for efficient management of soil and water resources. In addition, these changes in the transboundary Mekong River Basin (Mekong RB) are not well-known. The present study aimed to investigate the impacts of LULC changes on river discharge and sediment load in the Mekong RB during the period 1980–2015 using Soil and Water Assessment Tool (SWAT). The SWAT model was calibrated and validated using measured data of daily river discharge and monthly sediment load. Analysis of LULC change showed a 2.35% decrease in forest land and a 2.29% increase in agricultural land during the period of 1997–2010. LULC changes in 1997 and 2010 caused increases in river discharge and sediment load by 0.24 to 0.32% and 1.78 to 2.86%, respectively in the study region. Moreover, the river discharge and sediment load of the Mekong River have significantly positive correlation with agricultural land and negative correlation with forest land. The findings give beneficial insights to implement appropriate strategies of water and soil conservation measures to adapt and mitigate the adverse impacts of LULC in the Mekong RB. Further study will consider the impact of future LULC changes and uncertainties associated with the LULC projections for future management of soil and water conservation in the study region.

Concerning the significant increase in the negative effects of flash-floods worldwide, the main goal of this research is to evaluate the power of the Analytical Hierarchy Process (AHP), fi (kNN), K-Star (KS) algorithms and their ensembles in flash-flood susceptibility mapping. To train the two stand-alone models and their ensembles, for the first stage, the areas affected in the past by torrential phenomena are identified using remote sensing techniques. Approximately 70% of these areas are used as a training data set along with 10 flash-flood predictors. It should be remarked that the remote sensing techniques play a crucial role in obtaining eight out of 10 flash-flood conditioning factors. The predictive capability of predictors is evaluated through the Information Gain Ratio (IGR) method. As expected, the slope angle results in the factor with the highest predictive capability. The application of the AHP model implies the construction of ten pair-wise comparison matrices for calculating the normalized weights of each flash-flood predictor. The computed weights are used as input data in kNN–AHP and KS–AHP ensemble models for calculating the Flash-Flood Potential Index (FFPI). The FFPI also is determined through kNN and KS stand-alone models. The performance of the models is evaluated using statistical metrics (i.e., sensitivity, specificity and accuracy) while the validation of the results is done by constructing the Receiver Operating Characteristics (ROC) Curve and Area Under Curve (AUC) values and by calculating the density of torrential pixels within FFPI classes. Overall, the best performance is obtained by the kNN–AHP ensemble model.

There are currently only two methods (the within-growing season method and the inter-growing season method) used to analyse the normalized difference vegetation index (NDVI)–climate relationship at the monthly time scale. What are the differences between the two methods, and why do they exist? Which method is more suitable for the analysis of the relationship between them? In this study, after obtaining NDVI values (GIMMS NDVI3g) near meteorological stations and meteorological data of Inner Mongolian grasslands from 1982 to 2015, we analysed temporal changes in NDVI and climate factors, and explored the difference in Pearson correlation coefficients (R) between them via the above two analysis methods and analysed the change in R between them at multiple time scales. The research results indicated that: (1) NDVI was affected by temperature and precipitation in the area, showing periodic changes, (2) NDVI had a high value of R with climate factors in the within-growing season, while the significant correlation between them was different in different months in the inter-growing season, (3) with the increase in time series, the value of R between NDVI and climate factors showed a trend of increase in the within-growing season, while the value of R between NDVI and precipitation decreased, but then tended toward stability in the inter-growing season, and (4) when exploring the NDVI–climate relationship, we should first analyse the types of climate in the region to avoid the impacts of rain and heat occurring during the same period, and the inter-growing season method is more suitable for the analysis of the relationship between them.

The application of machine learning and deep learning in air pollution management is becoming increasingly crucial, as these technologies enhance the accuracy of pollution prediction models, facilitating timely interventions and policy adjustments. They also facilitate the analysis of large datasets to identify pollution sources and trends, ultimately contributing to more effective and targeted environmental protection strategies. Ho Chi Minh City (HCMC), a major metropolitan area in southern Vietnam, has experienced a significant rise in air pollution levels, particularly PM2.5, in recent years, creating substantial risks to both public health and the environment. Given the challenges posed by air quality issues, it is essential to develop robust methodologies for predicting PM2.5 concentrations in HCMC. This study seeks to develop and evaluate multiple machine learning and deep learning models for predicting PM2.5 concentrations in HCMC, Vietnam, utilizing PM2.5 and meteorological data over 911 days, from 1 January 2021 to 30 June 2023. Six algorithms were applied: random forest (RF), extreme gradient boosting (XGB), support vector regression (SVR), artificial neural network (ANN), generalized regression neural network (GRNN), and convolutional neural network (CNN). The results indicated that the ANN is the most effective algorithm for predicting PM2.5 concentrations, with an index of agreement (IOA) value of 0.736 and the lowest prediction errors during the testing phase. These findings imply that the ANN algorithm could serve as an effective tool for predicting PM2.5 concentrations in urban environments, particularly in HCMC. This study provides valuable insights into the factors that affect PM2.5 concentrations in HCMC and emphasizes the capacity of AI methodologies in reducing atmospheric pollution. Additionally, it offers valuable insights for policymakers and health officials to implement targeted interventions aimed at reducing air pollution and improving public health.

In recent years, droughts have strongly affected the Central Highlands of Vietnam and have resulted in crop damage, yield decline, and serious water shortage. This study investigated the livelihood vulnerability of five communities of farmers who are exposed to droughts in one of the more vulnerable regions of Vietnam—Dak Nong Province. A survey of 250 households was conducted in the five communities to collect data on the region’s sociodemographic profile, livelihood systems, social networks, health status, food and water security, drought conditions, and climate variability. Data were aggregated using a livelihood vulnerability index and the IPCC vulnerability index. The survey results indicate that Quang Phu community is the most vulnerable of the study’s communities, followed by Nam N’dir, Dak Nang, Duc Xuyen, and Dak D’ro in descending order of vulnerability. Water availability and livelihood strategies are the most important variables in determining the vulnerability of the five surveyed communities. In order to reduce vulnerability to droughts, water management practices and livelihood diversification in farming and nonfarming activities are recommended for the study area.

Given the present climate change context, accurate and timely coffee yield prediction is critical to all farmers who work in the coffee industry worldwide. The aim of this study is to develop and assess a coffee yield forecasting method at the regional scale in Dak Lak province in the central highlands of Vietnam using the Crop Growth Monitoring System Statistical Tool (CGMSstatTool—CST) software and vegetation biophysical variables (NDVI, LAI, and FAPAR) derived from satellite remote sensing (SPOT-VEGETATION and PROBA-V). There has been no research to date applying this approach to this specific crop, which is the main contribution of this study. The findings of this research reveal that the elaboration of multiple linear regression models based on a combination of information from satellite-derived vegetation biophysical variables (LAI, NDVI, and FAPAR) corresponding to the first six months of the years 2000–2019 resulted in coffee yield forecast models presenting satisfactory accuracy (Adj.R2 = 64 to 69%, RMSEp = 0.155 to 0.158 ton/ha and MAPE = 3.9 to 4.7%). These results demonstrate that the CST may efficiently predict coffee yields on a regional scale by using only satellite-derived vegetation biophysical variables. This study findings are likely to aid local governments and decision makers in precisely forecasting coffee production early and promptly, as well as in recommending relevant local agricultural policies.

This study presents hydrological impacts of future climate change (CC) and land use/cover change (LUCC) for the Srepok River Basin (SRB) in the Vietnam’s Central Highlands. The hydrology cycle of this basin was reproduced using Soil and Water Assessment Tool (SWAT) allowing an evaluation of hydrological responses to CC and LUCC. Future climate scenarios of the 2015–2100 period under Representative Concentration Pathways (RCP) 4.5 simulated by five General Circulation Models (GCMs) and LUCC scenario in 2050 were developed. Compared to the reference scenario (1980–2005), future LUCC increases the streamflow (0.25%) and surface runoff (1.2%) and reduces the groundwater discharge (2.1%). Climate change may cause upward trends in streamflow (0.1 to 2.7%), surface runoff (0.4 to 4.3%), and evapotranspiration (0.8 to 3%), and a change in the groundwater discharge (− 1.7 to 0.1%). The combination of CC and LUCC increases the streamflow (0.2 to 2.8%), surface runoff (1.6 to 5.6%), and evapotranspiration (1.0 to 3.1%), and reduces the groundwater discharge (1.5 to 2.7%) with respect to the reference scenario. Moreover, the results noted that the water scarcity may happen in the dry-seasonal months.