Download the cover page of the Journal of Climate Change Science: Here
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FOREWORD Pham Thi Thanh Nga Director-General, The Viet Nam Institute of Meteorology, Hydrology and Climate Change Chairperson of IHP Viet Nam and IHP Regional Steering Committee for Asia and the Pacific Download file: Here |
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APPLICATION OF NON-PARAMETRIC STATISTICAL METHODS FOR ANALYZING SEA SURFACE TEMPERATURE AND SALINITY TRENDS IN THE SOUTHERN COAST OF VIET NAM Tran Thi My Hong(1), Le Duc Quyen(2) Received: 10/8/2025; Accepted: 31/8/2025 Abstract: This study analyzes the long-term and seasonal trends of sea surface temperature and salinity from 2003 to 2023 at six monitoring stations located along the coast and on islands in Southern Viet Nam using non-parametric statistical methods, including the Mann-Kendall trend test and Sen’s slope estimator. These methods are well-suited for non-normally distributed and highly variable time series data. The results show a general warming trend in sea surface temperature, more pronounced during the rainy season, with statistically significant increases observed at Phu Quoc and Tho Chu, indicating regional ocean warming in the Southwestern coast, while Vung Tau exhibits a slight cooling trend. Sea surface salinity trends are more spatially heterogeneous, with Truong Sa being the only station showing a consistent and statistically significant decline in salinity across both seasons, whereas other stations display weak or statistically insignificant variations. These findings reflect differences in oceanographic dynamics, including the effects of rainfall, evaporation, ocean currents, and regional climate variability across the Southern coastal region and the East Sea. The study provides a valuable scientific basis for environmental monitoring, marine spatial planning, sustainable fisheries development, and climate change adaptation, and recommends enhancing data coverage through expanded observations, remote sensing integration, and regional ocean climate modeling to improve future trend assessments. Keywords: Sea surface temperature, sea surface salinity, non-parametric methods, climate change. Download file: Here |
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SEASONAL BASED-GLOBAL SENSITIVITY ANALYSIS OF SAC-SMA MODEL PARAMETERS Luong Tuan Trung, Ngo Thi Thuy, Luong Dung Huu Received: 3/9/2025; Accepted: 15/9/2025 Abstract: This study implemented Sobol-based global sensitivity analysis to assess parameter importance in the Sacramento Soil Moisture Accounting (SAC-SMA) model under contrasting regimes: Flood and dry seasons. The Mu Cang Chai watershed in Nam Mu basin is used as a case study. Results show clear seasonal contrasts in dominant processes and parameter interactions. In the flood season, runoff is strongly governed by lower-zone storages, with LZFSM, LZFPM, and LZTWM presenting the greatest influence. Interactions involving UZFWM further highlight the importance of upper-to-lower zone linkages in shaping flood flows. Calibration for wet conditions should therefore prioritize these storages, while handling percolation and drainage parameters as interaction-driven controls. In the dry season, UZFWM, LZTWM, and UZTWM are dominant parameters, while interaction effects involving impervious areas (PCTIM) also indicate that small surface conditions can modulate drought runoff. These seasonal insights support more efficient calibration to improve SAC-SMA model interpretability. Keywords: SAC-SMA, seasonal sensitivity analysis, Sobol. Download file: Here |
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ENHANCED DAILY CHIRPS PRECIPITATION USING SEQUENTIAL K-NEAREST NEIGHBORS CORRECTION AND KALMAN FILTER BLENDING FOR THAILAND Winai Chaowiwat, Jirayuth Srisat, Kritanai Torsri, Kanoksri Sarinnapakorn Received: 8/9/2025; Accepted: 22/9/2025 Abstract: Accurate precipitation data is essential for hydrological modeling and water resource management, particularly in tropical regions with complex topography and limited ground-based observation networks. This study develops an integrated two-stage framework combining K-nearest neighbors (KNN) machine learning bias correction with Kalman filter blending to enhance Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily precipitation estimates across Thailand’s diverse geographical and climatic conditions. The methodology utilized comprehensive meteorological data from 628 stations across Thailand spanning 44 years (1981-2024), with temporal partitioning into training (1981-2015) and validation (2016-2024) periods. The first stage implemented seasonal KNN bias correction using 11-dimensional feature vectors incorporating CHIRPS satellite precipitation, auxiliary meteorological variables (maximum/minimum temperature, relative humidity, evaporation), and station coordinates. The second stage applied adaptive Kalman filter blending with dual-update processing, combining raw CHIRPS data with KNN-corrected estimates. Results demonstrate exceptional performance improvements across both periods. Correlation coefficients increased dramatically from 0.42 to 0.94 during training (124% improvement) and from 0.41 to 0.91 during validation (122% improvement). Systematic bias correction transformed raw CHIRPS overestimation of 34.03% to controlled underestimation of -10.00% (BC CHIRPS) and -10.78% (BBC CHIRPS) during training, with similar validation patterns. Regional analysis revealed differential effectiveness across Thailand’s climatic zones. The most challenging DJF dry season showed severe raw CHIRPS overestimation of 588.27% (training) and 167.12% (validation), reduced by 95-99% with both corrections. Spatial validation confirmed operational applicability, effectively eliminating widespread overestimation while preserving legitimate precipitation signals. The integrated framework successfully addresses systematic biases in satellite precipitation products while maintaining computational efficiency. This research demonstrates that sophisticated machine learning integrated with optimal filtering theory can significantly enhance satellite precipitation accuracy for operational applications in data-scarce tropical regions, with demonstrated effectiveness across Thailand’s diverse conditions and strong potential for broader tropical applications. Keywords: CHIRPS bias correction, K-nearest neighbors (KNN), Kalman filter blending, satellite precipitation, machine learning, Thailand. Download file: Here |
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REMOTE SENSING – BASED WATER DEMAND INDEX MAPPING: INSIGHTS FROM KHANH HOA PROVINCE, VIET NAM Ngo Thi Thuy(1), Luong Tuan Trung(1), Vu Thi Thuy(1), Received: 8/7/2025; Accepted: 28/8/2025 Abstract: Located on the South-Central Coast of Viet Nam, the former Ninh Thuan (now is Khanh Hoa province) is one of the driest areas in Viet Nam and frequently experiences severe water shortages during the dry season. This study aims to develop spatial maps of the Water Demand Index (WDI) for Ninh Thuan area during the dry months from March to August in the representative drought year of 2020. The methodology integrates big Earth data and GIS data to compute WDI using key influencing first-order variables, namely soil moisture (SM), vegetative growth (NDVI), and heat factor (growing degree day, GDD). WDI reflects the spatial imbalance between vegetation water demand and the available soil water in areas with condensed agricultural activity at each pixel level. The WDI maps developed for the agricultural areas of Ninh Thuan reveal substantial spatial and temporal variability. Agricultural zones located in former Ninh Son, Thuan Bac, and Ninh Phuoc districts generally exhibit high WDI values (above 250), indicating considerable water stress. In contrast, certain regions such as the former Thuan Nam and Ninh Hai districts show comparatively lower WDI values. The WDI values peaked in April and May, coinciding with the most intense drought period and the critical crop development stage. This study provides a practical and spatially explicit tool for monitoring drought conditions and support effective water allocation planning amid the growing challenges of climate change and increasing water scarcity in the South-Central Coast of Viet Nam. Keywords: Water Demand Index, Ninh Thuan area, remote sensing. Download file: Here |
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COPULA-BASED CONSTRUCTION OF AN INTEGRATED DROUGHT INDEX IN THE BA RIVER BASIN, VIET NAM Do Thi Ngoc Bich(1), Nguyen Tu Anh(1), Nguyen Thanh Long(1), Received: 14/8/2025; Accepted: 5/9/2025 Abstract: Drought is a complex natural hazard characterized by stochastic occurrence, wide-ranging impacts, and sequential propagation across the hydrological cycle. This study proposes a copula-based framework to construct an Integrated Drought Index (IDI) for the Ba River Basin, Viet Nam, combining meteorological (SPI), agricultural (SMI), and hydrological (SRI, SGI) drought dimensions. The VIC distributed hydrological model was developed and calibrated for 1980-2023, explicitly incorporating the operation of major reservoirs to better capture regulated flow regimes. Standardized drought indices were computed at monthly timescales, and a Clayton Copula was applied to model lower-tail dependence among them, enabling quantification of compound drought conditions. The resulting IDI was analyzed for four representative drought years (2015, 2016, 2019, 2020), which correspond to major El Nino episodes associated with severe rainfall deficits and socio-economic damages. Results show that the IDI effectively captures both the intensity and spatial extent of drought propagation, with 2016 emerging as the most extreme basin-wide drought, 2020 showing similarly widespread impacts, and 2015 and 2019 characterized by more localized drought hotspots. By integrating reservoir regulation, multi-source data, and copula-based dependence modeling, this study provided a more holistic representation of compound drought risk in a socio-hydrologically complex basin. Keywords: VIC model, Integrated Drought Index (IDI), Clayton Copula, Ba River Basin. Download file: Here |
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ANALYSIS OF EXTREME RAINFALL SCENARIOS AND LANDSLIDE SUSCEPTIBILITY IN THE MOUNTAINOUS REGION OF PHU THO PROVINCE UNDER THE IMPACTS OF CLIMATE CHANGE Doan Ha Phong(1), Doan Tran Anh(2), Ta Thu Hang(1) Received: 30/7/2025; Accepted: 22/8/2025 Abstract: This study assesses the impacts of climate change on rainfall patterns and landslide risk in Cao Phong District, Phu Tho Province, with a particular focus on Bac Phong commune – a high-risk area. Climate data were bias-corrected from three regional climate models (PRECIS, CCAM, clWRF) under two emission scenarios (RCP4.5 and RCP8.5), using quantile mapping for statistical adjustment. Analyses for the periods 2016-2035, 2046-2065, and 2080-2099, relative to the baseline (1986-2005), reveal a pronounced increase in annual precipitation. Under RCP4.5, rainfall is projected to increase by +2.3% to +11.7%, while under RCP8.5, increases reach up to +12.5% by the end of the century. Rainfall during the wet season also rises substantially, by approximately +10.6-10.7%. Extreme rainfall analysis indicates that about 82.5% of the district’s area is exposed to daily precipitation exceeding 270 mm – a threshold that triggers landslides in steep terrain with limited vegetation cover. Three rainfall-landslide hazard scenarios were developed: Low (P20), mean, and high (P80). Integrating these scenarios with topographic, geological, and land-use factors shows that areas of high and very high landslide susceptibility may account for more than 50% of the total area, concentrated mainly in the Southeastern part of the commune. The resulting landslide hazard zoning map provides an essential tool for early warning, climate-adapted planning, and disaster risk management in the context of climate change. Keywords: Climate change, extreme rainfall, landslide susceptibility, RCP scenarios, Cao Phong District. Download file: Here |
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VULNERABILITY ASSESSMENT OF CROPS DUE TO SALINE INTRUSION IN VINH LONG PROVINCE Nguyen Van Hong(1), Le Xuan Hoa(1), Bui Chi Nam(1), Nguyen Thao Hien(1), Received: 4/8/2025; Accepted: 29/8/2025 Abstract: Saline intrusion has become one of the most serious threats to agriculture in the Mekong Delta, yet most existing studies have focused on regional or river-basin scales, with limited attention to local-level differences. Vinh Long Province, where crop structures are diverse and highly sensitive to salinity, has received little systematic vulnerability assessment despite being increasingly exposed to salinity stress. This study aims to fill this gap by assessing the vulnerability of major crops to saline intrusion in Vinh Long Province, focusing on dry-season cultivation during 2021-2023. Input data include statistical yearbooks, expert consultation, and 128 field questionnaires. The methodology applies the AHP-based Vulnerability Index approach, integrating three components: Exposure, Sensitivity, and Adaptive Capacity. Results show that Vung Liem and Tra On districts exhibit high vulnerability (Vi > 0.6), while Vinh Long city is least affected. The findings not only highlight spatial differences in vulnerability but also provide a scientific basis for designing locally tailored adaptation strategies in agriculture. Keywords: Exposure, sensitivity, adaptive capacity, vulnerability, Vinh Long. Download file: Here |
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ASSESSING WATER SECURITY UNDER LONG-TERM CLIMATE TO SUPPORT WATER MANAGEMENT: A CASE STUDY FROM BEGA-BROGO RIVER NSW P. Cu, J. Simpson, D. Dutta Received: 4/8/2025; Accepted: 17/8/2025 Abstract: In New South Wales (NSW), water resources are governed by water sharing plans (WSPs). WSPs are legally binding instruments under the Water Management Act 2000 that regulate how water is allocated between environmental needs and consumptive uses. As the cornerstone of NSW’s broader water management framework, WSPs establish rules for sharing water across social, economic, agricultural, cultural, and environmental domains. Many waterways in NSW Coastal region have been classified as being under high or medium hydrological and environmental stress. While there is enough water in the region to meet agricultural demands on an annual basis, most extraction takes place in the drier summer period when temperatures are high and flows are low. This puts high stress on the flora and fauna that rely on the rivers. Longer droughts and reduced flows due to climate variability and change will amplify these impacts. The NSW Government has invested in new climate datasets and improved modelling that provide a more sophisticated understanding of historic climate variability in the South Coast region, as well as likely future climate risks moving from making decisions that are based largely on single worst-case scenarios to a much more comprehensive understanding of natural variability and potential extreme events based on modelling using daily stochastic data of 13,000 years. This paper presents a case study of the Bega-Brogo River System, detailing how water is allocated under the relevant WSPs. The paper further examines water security analysis under long-term climate variability. The impact of climate change and East Coast Lows on water availability and allocation within the catchment is also assessed to better understand the climate risks faced by water users and the environment. Keywords: Water sharing plan, Bega-Brogo river system modelling, water resources management, climate change, water security. Download file: Here |
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INDICATOR-BASED ASSESSMENT OF POST-DISASTER RECOVERY CAPACITY OF THE MIGRANT HOUSEHOLDS: A CASE STUDY OF TYPHOON YAGI IN 2024 Nguyen Thi Dung(1), Nguyen Thi Lien(2), Tetsuji Ito(3), Ta Thi Hoai(4), Nguyen Thi Hoang Ha(1) Received: 27/5/2025; Accepted: 13/6/2025 Abstract: Enhancing post-disaster recovery capacity is crucial for reducing disaster risk as climate-driven disasters intensify. This study examines the capacity of post-disaster recovery of migrant households in a ward in Hanoi, Viet Nam, after historical Typhoon Yagi in 2024. The study adopted a framework with 34 indicators across four dimensions, including housing recovery, economic stability, public service accessibility, and social cohesion. Data were collected via observation, in-depth interviews with 17 local authorities and residents, and semi-structured interviews with 57 migrants. The data were normalized on a 0-1 scale, where 0 and 1 represent the lowest and highest capacity, respectively. Results showed that public service accessibility scored relatively high (0.8) due to efficient and reliable public services supporting migrants. In contrast, social cohesion scored low (0.3) due to limited social support and community engagement. Housing recovery and economic stability scored 0.4, indicating persistent challenges in home repair and financial resources after the disaster. Overall, the capability of post-disaster recovery of migrant families was moderate at 0.5. In-depth interviews underscored these households’ vulnerability, reflecting economic instability and poor housing conditions. These findings provide a scientific basis for developing disaster recovery plans and formulating strategies and policies that address the needs of migrant communities in disaster risk reduction and climate change mitigation. Keywords: Indicator-based assessment, migrant household, post-disaster recovery, typhoon Yagi. Download file: Here |
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DEVELOPMENT OF FLOOD RISK WARNING SYSTEM FOR RIVER BASINS OF VIET NAM, CASE STUDY IN THE CAI NHA TRANG RIVER BASIN Luong Huu Dung, Chu Nguyen Ngoc Son, Luong Tuan Trung, Duong Hong Nhung, Ngo Thi Thuy Received: 29/7/2025; Accepted: 13/8/2025 Abstract: Flooding is one of the most serious natural hazards affecting river basins in Viet Nam, particularly in urban and coastal areas. This paper presents the development of a flood risk warning system for the Cai Nha Trang River Basin. In this study, flood risk is quantified based on the combination of three components: Hazard, exposure, and vulnerability. Hydrological and hydraulic models were applied to simulate flood hazards, while socio-economic and land use data were used to assess exposure and vulnerability. These layers were integrated into a WebGIS platform that allows real-time monitoring of rainfall and water levels from observation stations, and provides flood risk assessment under different rainfall and flood scenarios. The system not only visualizes the spatial distribution of risks but also supports timely warnings and decision-making. Results from the Cai Nha Trang case study suggest that the proposed approach can be an effective tool to improve preparedness and strengthen resilience in flood-prone basins of Viet Nam. Keywords: Flood risk, Early warning system, Cai Nha Trang River basin. Download file: Here |
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INFLUENCE OF VERTICAL BREAKWATER WALL ROUGHNESS ON WAVE-INDUCED OVERTOPPING: A CFD-BASED STUDY Le Quoc Huy(1), Le Duc Quyen(1), Tran Thi My Hong(2) Received: 28/7/2025; Accepted: 15/8/2025 Abstract: In this study, we analyzed the wave dynamics in a nearshore region protected by a vertical breakwater through computational fluid dynamics (CFD) simulations. The focus was placed on evaluating the role of vertical wall roughness in influencing wave energy dissipation and overtopping behavior when waves encounter coastal structures. Four wall conditions were modeled with varying roughness coefficients: A smooth wall (NR = 0.0), and progressively rougher walls denoted as WR1 (0.5), WR2 (0.75), and WR3 (1.0). These cases were designed to systematically assess how increased surface roughness affects the hydrodynamic response of waves in front of and behind the breakwater. The simulation results demonstrated a clear trend: As wall roughness increased, the overtopping water depth consistently decreased. Specifically, the overtopping values were 0.083 m for the smooth wall (NR), followed by 0.082 m, 0.081 m, and 0.0719 m for WR1, WR2, and WR3, respectively. This suggests that increased wall roughness enhances wave energy dissipation, thereby reducing the volume of water overtopping the structure. These findings highlight the critical role of structural surface characteristics in coastal defense design. Incorporating surface roughness into vertical breakwater modeling can contribute to more effective wave energy attenuation, potentially improving the resilience and performance of coastal protection systems under wave impact. Keyword: Vertical wall, overtopping, VoF model, Stokes wave. Download file: Here |
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