Climate and land use changes are major factors affecting runoff in regional basins. Understanding this variation through considering the interactions among hydrological components is an important process for water resource management. This study aimed to assess the variation of future runoff in the Upper Chi Basin, Northeastern Thailand. The QSWAT hydrological model was integrated into three CMIP6 GCMs—ACCESS-CM2, MIROC6, and MPI-ESM1-2-LR—under SSP245 and SSP585 scenarios for the period 2023–2100. The Land Change Modeler (LCM) was also used for future land use simulation. The results revealed that the future average long-term precipitation and temperature tended to increase while forest land tended to decrease and be replaced by sugarcane plantations. The accuracy assessment of the baseline year runoff calculation using QSWAT for the period 1997–2022 showed an acceptable result, as can be seen from the R², NSE, RSR, and PBIAS indices. This result could lead to the temporal and spatial simulation of future runoff. Likewise, the runoff of the two SSP scenarios tended to increase consecutively, especially in the SSP585 scenario. In addition, in cases of long-term spatial changes in the subbasins scale, over 90% of the area—from upstream to the outlet point—tended to be higher due to two major factors; namely, future increased precipitation and changes in cultivation, which would be influential to groundwater and interflow components, respectively. The methodology and result of this study can be useful to stakeholders in understanding changes in hydrological systems so that they can apply it to developing a strategy for water resource management and to handling factors affecting different dimensions properly and sustainably.

This study focuses on assessing flash flood risks in Northeastern Thailand, particularly within the Lam Saphung, Phrom, and Chern River Basins, which are highly susceptible to flash floods and debris flows. Using the HEC-RAS hydraulic model integrated with GIS tools, the research analyzes historical and scenario-based flood events to evaluate the impact of land use changes and hydrological dynamics. The model was calibrated and validated with statistical metrics such as R² values ranging from 0.745 to 0.994 and NSE values between 0.653 and 0.893, indicating strong agreement with the observed data. This study also identified high-risk areas, with up to 5.49% and 5.50% increases in flood-prone areas in the Phrom and Chern River Basins, respectively, from 2006 to 2019. Key findings highlight the critical role of proactive risk management and targeted mitigation strategies in enhancing community resilience. The integration of advanced hydraulic modeling with detailed datasets enables precise flood hazard mapping, including flood depths exceeding 1.5 m in certain areas and high-risk zones covering up to 105.2 km² during severe flood events. These results provide actionable insights for emergency response and land use planning. This research significantly contributes to hydrological risk assessments by advancing modeling techniques and delivering practical recommendations for sustainable flood management. The outcomes are particularly relevant for stakeholders, including urban planners, emergency management officials, and policymakers, who aim to strengthen resilience in vulnerable regions. By addressing the complexities of flash flood risk assessments with robust quantitative evidence, this study not only enhances the understanding of flood dynamics, but also lays the groundwork for developing adaptive strategies to mitigate the adverse impacts of flash floods, safeguarding both communities and infrastructure in the region.

Climate change results in reservoir management challenges, especially in areas with a high risk of drought and flooding. Traditional reservoir rule curves are insufficient for addressing variations in reservoir inflow. This study presents a framework combining GCMs from CMIP6 (ACCESS-CM2, MIROC6, and MPI-ESM1-2-LR) under SSP2-4.5 and SSP5-8.5 scenarios and WEAP, the accuracy of which has been validated for reservoir inflow and storage capacity. This framework is integrated with Hippopotamus Optimization (HO) to develop a resilience reservoir rule curve (RRRC) for the Ubolrat Reservoir for 2024–2055, employing a dual-objective function that emphasizes reducing water shortages and water excess. The results indicate that the RRRC developed via HO is more efficient and suitable than Honeybee Mating Optimization (HBMO) and existing rule curves. When tested with historical inflow data, HO reduced the average water shortage by 50% and the maximum shortage period by 79% compared to the existing rule curve. Under future climate scenarios (SSP2-4.5 and SSP5-8.5), efficiency improved significantly, achieving a water shortage reduction of 95–98% and a shortage period reduction of 83–88%. Additionally, HO demonstrated outstanding efficiency in water excess management, with a 7–11% reduction in average excess water. This potential reflects its adaptability in the context of future variations in hydrological conditions. This crucial finding illustrates that the integrated framework can develop resilient rule curves even under uncertainty. HO integrated with various models can be implemented as an optimal framework with high potential for reservoir operation planning under climate change. The developed methodology can be implemented in other reservoirs to investigate additional factors for the sustainable promotion of water resource resilience.

The purpose of this study is to investigate the future runoff into the Nong Han Lake under the effects of climate change. The hydrological model, Soil and Water Assessment Tool (SWAT) has been selected for this study. The calibration and validation were performed by comparing the simulated and observed runoff from gauging station KH90 for the period 2001–2003 and 2004–2005, respectively. Future climate projections were generated by Providing Regional Climates for Impacts Studies (PRECIS) under the A2 and B2 scenarios. The SWAT model yielded good results in comparison to the baseline; moreover, the results of the PRECIS model showed that both precipitations and temperatures increased. Consequently, the amount of runoff calculated by SWAT under the A2 and B2 scenarios was higher than that for the baseline. In addition, the amount of runoff calculated considering the A2 scenario was higher than that considering the B2 scenario, due to higher average annual precipitations in the former case. The methodology and results of this study constitute key information for stakeholders, especially for the development of effective water management systems in the lake, such as designing a rule curve to cope with any future incidents.

Water shortage is a critical problem that affects the sustainability of the agricultural sector in Northeastern Thailand, especially areas located far from major rivers. This study developed an integrated QSWAT–WEAP modeling system combined with GIS-based spatial analysis for water shortage risk assessment at the sub-district level in Maha Sarakham Province, covering 5292 sq.km and 133 sub-districts. The system bridges the institutional gap between hydrological sub-watershed boundaries and administrative jurisdictions, enabling model outputs that directly support local governance decision-making. The QSWAT model simulated sub-watershed streamflow while the WEAP model evaluated water balance against water demand from five sectors. QSWAT validation (2017–2023) achieved R² = 0.72, NSE = 0.70, and RSR = 0.54, while WEAP verification yielded R² of 0.66–0.80 and NSE of 0.65–0.71. In the spatial analysis, 106 of 133 sub-districts (79.7%) experienced low water availability. Agriculture was the dominant sector in terms of water demand, which has increased at an average rate of 1.7% annually and accounted for 88.7% of total demand in the study period. According to the temporal analysis, the dry season was the most critical period, with peak water shortage of 99.2% in March. Overall, 105 of 133 sub-districts (78.9%) were classified as having water shortages of moderate to very high severity. These findings provide a quantitative basis for sustainable water resource management planning and drought mitigation, thus helping to enhance agricultural sustainability in Maha Sarakham Province.

Water shortage is a major problem that affects sustainability of agricultural sector in northeastern Thailand especially areas remote from a main river. This study aims at developing a system for water shortage risk assessment at sub-district level in Maha Sarakham Province. QSWAT model for sub-watershed streamflow simulation was integrated with WEAP model to analyze water balance and assess water shortage based on water demand from five sectors: non-irrigated agriculture, irrigated agriculture, consumption, services, and industry. The findings revealed that both models provided the results of calibration, and the validated results of streamflow analysis were at satisfactory to good level. From spatial analysis, distribution of provincial water resource was significantly different. Sub-districts located along the Chi River had high streamflow but low water shortage while those from central to southern part had limited streamflow although water demand was high especially for agricultural sector. According to temporal analysis, critical period of water shortage was found in the dry season and seasonal transition after post-harvest period. Integration of data on streamflow and water demand can be useful to divide subdistricts into three group: positive water balance, balance, and negative water balance. Most sub-districts were classified as negative water balance that needed urgent measures for water resource development. These findings have provided important data for planning water resource management locally and supporting development of drought mitigation measures for vulnerable areas in Maha Sarakham Province.