Soil and water loss is a severe environmental problem in tropical and subtropical Asia (TSA). This review systematically summarizes the techniques that have been widely applied in the TSA region and compares the conservation efficiency of these techniques based on the runoff and sediment reduction ratios (η_r and η_s). The results show that the current techniques can be divided into biological, engineering and agricultural practice measures, and in most cases, their efficiencies in reducing sediment loss (η_s = 14.0–99.5%, 61.3–100.0% and 0.6–95.4%, respectively) were higher than in reducing runoff loss (η_r = 2.8–9.38%, 0.28–83.3% and 1.62–70.2%, respectively). Monocultures of single tree species (e.g., Pinus massoniana) sometimes showed very limited conservation effects. Vetiver and alfalfa were more effective at reducing soil loss than other hedgerow species. Contour tillage, ridge farming, and reduced tillage generally showed high efficiencies in reducing soil loss compared with other agricultural practice measures. The combination of engineering and biological techniques could more effectively reduce soil and water loss compared with the application of these techniques along. Future works should be conducted to build unified technical standards and reasonable comprehensive evaluation systems, to combine these techniques with environmental engineering technologies, and to develop new amendment materials.

Vu Gia-Thu Bon (VGTB) river basin is an area where flash flood and heavy flood events occur frequently, negatively impacting the local community and socio-economic development of Quang Nam Province. In recent years, structural and non–structural solutions have been implemented to mitigate damages due to floods. However, under the impact of climate change, natural disasters continue to happen unpredictably day by day. It is, therefore, necessary to develop a spatial decision support system for real-time flood warnings in the VGTB river basin, which will support in ensuring the area’s socio-economic development. The main purpose of this study is to develop an online flood warning system in real-time based on Internet-of-Things (IoT) technologies, GIS, telecommunications, and modeling (Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center’s River Analysis System (HEC–RAS)) in order to support the local community in the vulnerable downstream areas in the event of heavy rainfall upstream. The structure of the designed system consists of these following components: (1) real-time hydro-meteorological monitoring network, (2) IoT communication infrastructure (Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), wireless networks), (3) database management system (bio-physical, socio-economic, hydro-meteorological, and inundation), (4) simulating and predicting model (SWAT, HEC–RAS), (5) automated simulating and predicting module, (6) flood warning module via short message service (SMS), (7) WebGIS, application for providing and managing hydro-meteorological and inundation data, and (8) users (citizens and government officers). The entire operating processes of the flood warning system (i.e., hydro-meteorological data collecting, transferring, updating, processing, running SWAT and HEC–RAS, visualizing) are automated. A complete flood warning system for the VGTB river basin has been developed as an outcome of this study, which enables the prediction of flood events 5 h in advance and with high accuracy of 80%.

Optimal Selection of Number and Location of Meteo-Hydrological Monitoring Networks on Vu Gia – Thu Bon River Basin using GIS

This study applied machine learning/ deep learning, and geographic information systems to predict saltwater intrusion in Ho Chi Minh City, Vietnam using monthly salinity, temperature, rainfall, and tide datasets between 2008 and 2024. The results indicated that the Long Short-Term Memory outperformed the Random Forest model with higher accuracy across almost stations – five out of six stations, particularly at An Ha station (NSE = 0.92 and PBIAS = -2.12%). In the future, the forecasting results show that serious salinity intrusion in coastal areas such as Can Gio district, and there is a risk of spreading deep into the area around Nha Be, and Ong Thin stations. Specifically, salinity peaks in 2029 (spread fast and deep with peak value of 13.84‰) and 2033 (spread slowly with peak value of 14.16‰), following by a slight decline in 2035. Moreover, using geographic information systems to spatial analysis highlights high-risk zones, contributing to support the management of water resources, sustainable urban planning orientation and promoting adaptive solutions to protect the ecosystem and livelihoods of the coastal residential community affected.