Le Hoang Tu

Rainfall map plays a crucial role in describing the spatial distribution of rainfall from discrete rain gauge station data as input for water resource-related issues such as flooding, especially in large urban areas like Ho Chi Minh City. This research applied interpolation techniques in GIS to generate a rainfall map for Ho Chi Minh City. The research used three interpolation techniques including IDW, Spline, and Ordinary Kriging (OK), with two neighborhood points settings . The results illustrated that the interpolated rainfall maps varied depending on the interpolation techniques and two neighborhood points settings. IDW interpolation technique produced interpolated rainfall maps dependent on location and distance of the rain gauge stations. Meanwhile, Spline and OK techniques created rainfall with clearer spatial orientation and smoother rainfall variation. The values of R ² (near 0), RMSE (>300 mm) and RSR (>1.0) indices indicated that all interpolation techniques generally have low numerical accuracy. This low predictive skill may be primarily attributed to the low density of the rain gauge network, given the complexity of the study area. The generation of spatial rainfall maps using these interpolation techniques in the study area requires careful consideration to ensure reliable results.

Kon Tum is known as a fertile plateau region with a wide variety of crops, but it has faced serious challenges regarding irrigation water resources under climate change conditions. Currently, the agricultural sector plays a crucial role in the socio-economic development of the province. Meanwhile, a lack of irrigation water will affect the productivity and quality of agricultural products, leading to unsustainable agricultural production and reduced socio-economic development. The major crops of the province, including winter-spring rice, summer-autumn rice, cassava, and rubber, were simulated in this research for their irrigation water demands by using the FAO CROPWAT 8.0 model. This research calculates reference evapotranspiration (ET0) through the FAO Penman-Monteith approach, effective rainfall, and crop water requirements based on climatic data from 1990 to 2023, soil properties, and crop-specific parameters. Large differences in irrigation demand among crop types and growth stages were depicted. Winter-spring rice had the highest irrigation requirement, amounting to 709.7 mm, followed by rubber with 487.9 mm, summer-autumn rice with 323.1 mm, and cassava with 129.6 mm. However, the total amount of annual irrigation water demand of the studied crops reached about 538.4 million m ³ , in which rubber accounted for 71.7%, winter-spring rice accounted for 9.6%, summer-autumn rice for 9.3%, and cassava for 9.5%. The key parameters of irrigation demand included climatic conditions, crop coefficient, and soil waterholding capacity. The results may provide an important basis necessary for policies on sustainable agricultural water management and climate adaptation strategy in this region.

Mangroves provides essential ecosystem services such as protection of coastal areas, carbon sequestration, and habitat provision for diverse species in coastal ecosystems. Species distribution models (SDMs) are powerful tools for predicting the potential distribution of mangrove species, which support impact assessments of climate changes on biodiversity and ecological functions of mangrove ecosystems. A comprehensive dataset for mangrove occurrence information derived from the Forest Inventory Map of Vietnam was designed to facilitate the building and projection of SDMs. The prediction data designed for training SDMs integrates ecological information including 701 field survey-based mangrove occurrences at the genus level and 21 environmental variables such as bioclimatic variables, digital elevation model and soil properties with 1 km spatial resolution. The projection data for provide sets of predictors aligned with four shared socioeconomic pathways scenarios representing two future periods to support the projection of SDM results under future climate conditions in Vietnam. This dataset serves as a valuable ecological information resource, enabling the modeling and predicting of potential mangrove habitats and distributions for the protection and restoration of mangroves in Vietnam under changing environmental conditions.

Flood hazards have led to substantial fatalities and economic loss in the last five decades, making it essential to understand flood dynamics in a warming climate. This study reports the first comprehensive assessment of projected flood hazards across Vietnam. We used downscaled climate data from the CMIP6 initiative, involving a total of 20 climate models, and streamflow projection simulated using a semi-distributed hydrological model. The assessment covers seven near-natural catchments, each representing a climate zone of the country. To evaluate climate change impacts on floods, the study simultaneously analyzes changes in three indices: (i) the annual hottest day temperature, to represent temperature extremes; (ii) the maximum daily rainfall amount, to represent rainfall extremes; and (iii) the discharge value exceeding 5% in a year, to assess streamflow extremes. Changes in the selected indices (relative to the reference period from 1985 to 2014) are assessed under four emission scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) and two future time slices (2036–2065 and 2070–2099). Although the robustness (as indicated by multi-model agreement) and significance (identified through the statistical test) of the changes vary substantially, depending on the selected indices and assessed time slices, an overall increase is consistently identified across all of the assessed hydro-climatic extremes (up to 4.8 °C for temperature extremes, 43 mm for rainfall extremes, and 31% for streamflow extremes). The findings suggest a potential increase in flood risk across Vietnam in a warming climate, highlighting the urgent need for improved flood preparedness and investment to reduce economic loss and mortality in an uncertain future.

Remotely sensed evapotranspiration (ET _RS ) is increasingly used for streamflow estimation. Earlier reports are conflicting as to whether ET _RS is useful in improving streamflow estimation skills. We believe that it is because earlier works used calibrated models and explored only small subspaces of the complex relationship between model skills for streamflow (Q) and ET. To shed some light on this complex relationship, we design a novel randomized, large sample experiment to explore the full ET-Q skill space, using seven catchments in Vietnam and four global ET _RS products. For each catchment and each ET _RS product, we employ 10,000 SWAT (Soil and Water Assessment Tool) model runs whose parameters are randomly generated via Latin Hypercube sampling. We then assess the full joint distribution of streamflow and ET skills using all model simulations. Results show that the relationship between ET and streamflow skills varies with regions, ET _RS products, and the selected performance indices. This relationship even changes with different ranges of ET skills. Parameter sensitivity analysis indicates that the most sensitive parameters could have opposite contributions to ET and streamflow skills. Conditional probability assessment reveals that with certain ET _RS products, the probabilities of having good streamflow skills are high and increase with better ET skills, but for other ET _RS products, good model skills for streamflow are only achievable with certain intermediate ranges of ET skills, not the best ones. Overall, our study provides a useful approach for evaluating the value of ET _RS for streamflow estimation.

The impact of digital elevation model data sources on identifying catchment boundary in Vietnam