Quantifying the intensity and frequency of climatic extremes under the impacts of climate change is crucial for effective water resource management. In this study, we utilize the Soil and Water Assessment Tool (SWAT) hydrological model, robust indices, e.g., Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) as well as the Interquartile Range (IQR) method for a comprehensive analysis of the river flow response to future climate scenarios towards 2090. Four General Circulation Models (GCMs) under two Shared Socioeconomic Pathways (SSPs) have been used, including BCC-CSM2-MR, CanESM5, MIROC6, and MRI-ESM2-0. We aim to reveal the future impacts of extreme events and their potential consequences for local livelihoods and human well-being in the Srepok River basin—a major tributary of the Mekong River basin in Southeast Asia. Our findings include (1) a significant discrepancy between extreme events found with more flood events projected towards 2090; (2) a shift in precipitation patterns with an increase in intensity is observed; and (3) a correlation between climatic extremes and regional characteristics has been identified. This work provides valuable insights into the anticipated changes in climatic extremes under the impacts of climate change and serves as the scientific basis for stakeholders and decision-makers to develop adaptative strategies and sustainable plans to enhance the region's resilience.

Despite its energy benefits, hydropower dam development often causes ecological damages and social disruption, including downstream livelihood impacts, and biodiversity loss. Current methods for analyzing changes in downstream inundation extent due to dam operation typically rely on historical ground or satellite observations, or on coupled hydrological‐hydrodynamic modeling. However, while the former fails to isolate hydropower impacts from climate variations, the latter suffers from extensive input data requirements and high computational burden. This study proposes a novel hybrid framework integrating satellite data‐driven Forecasting Inundation Extents using REOF (Rotated Empirical Orthogonal Function) analysis (FIER), and the process‐based Hydrological Predictions for the Environment (HYPE) model incorporating the Integrated Reservoir Operation Scheme (IROS). The framework enables the isolated assessment of long‐term hydropower impacts on downstream inundation dynamics with computational efficiency and reduced ground data requirements, making it suitable for poorly gauged regions. Applying FIER‐HYPE‐IROS to the Lower Mekong River basin (LMB), a region significantly affected by dam proliferation impacting fisheries and agriculture, we found that dam operations decreased decadal‐average wet season water levels by up to 5% and increased dry season levels by up to 11%. Wet season inundation occurrence decreased by 11 days and the inundated area by 6%, while dry season inundation occurrence extended by 6 days and the surface water area increased by 40%. Although the current framework does not explicitly assess the downstream hydrological modifications, it offers a cost‐effective alternative for evaluating upstream alterations on inundation dynamics, such as dam operations, particularly in poorly gauged regions.

Flood inundation mapping (FIM), which is essential for effective disaster response and management, requires rapid and accurate delineation of flood extent and depth. Remote sensing FIM, especially using satellite imagery, offers certain capabilities and advantages, but also faces challenges such as cloud and canopy obstructions and flood depth estimation. This research developed a novel hybrid approach, named FLDSensing, which combines remote sensing imagery with the FLDPLN (pronounced “floodplain”) flood inundation model, to improve remote sensing FIM in both inundation extent and depth estimation. The method first identifies clean flood edge pixels (i.e., floodwater pixels next to bare ground), which, combined with the FLDPLN library, are used to estimate the water stages at certain stream pixels. Water stage is further interpolated and smoothed at additional stream pixels, which is then used with an FLDPLN library to generate flood extent and depth maps. The method was applied over the Verdigris River in Kansas to map the flood event that occurred in late May 2019, where Sentinel-2 imagery was used to generate remote sensing FIM and to identify clean water-edge pixels. The results show a significant improvement in FIM accuracy when compared to a HEC-RAS 2D (Version 6.5) benchmark, with the metrics of CSI/POD/FAR/F1-scores reaching 0.89/0.98/0.09/0.94 from 0.55/0.56/0.03/0.71 using remote sensing alone. The method also performed favorably against several existing hybrid approaches, including FLEXTH and FwDET 2.1. This study demonstrates that integrating remote sensing imagery with the FLDPLN model, which uniquely estimates stream stage through floodwater-edges, offers a more effective hybrid approach to enhancing remote sensing-based FIM.