Wetlands play a crucial role in regulating regional climate through their effects on surface energy balance and evapotranspiration. However, rapid wetland loss in Southeast Asia has raised concerns about its potential to intensify extreme heat exposure during heatwaves. This study investigates the impact of wetland loss on regional temperature and human heat stress during the 2023 heatwave in the Cambodia Lower Mekong Delta (CLMD). Using the WRF model coupled with the Noah-MP land surface scheme, we conducted high-resolution time-lagged ensemble simulations (30 members per scenario) under three land cover scenarios: baseline, historical wetland extent, and current wetland extent, with wetland land-cover properties prescribed and soils initialized at full saturation to represent an idealized upper-bound thermodynamic scenario. Additional simulations were performed using a Pseudo Global Warming (PGW) approach with monthly mean temperature deltas derived from 29 CMIP6 global climate models under SSP2-4.5. Results show ensemble-mean near-surface cooling of 2.9 °C (historical extent) and 2.3 °C (current extent) over wetland-covered areas, with cooling extending beyond wetland boundaries; peak-hour spatial cooling ranges from 1 to 5 °C at the event maximum. Under the temperature-perturbed PGW simulations, similar spatial cooling patterns persist, indicating that the modeled cooling response continues under the applied thermodynamic warming. The combined scenario of climate warming and wetland loss indicates a potential for amplified heat exposure. Although the reduction in simplified Wet Bulb Globe Temperature (sWBGT) under the wetland scenarios was modest (1–2 °C) and did not shift stress categories, it remains relevant in terms of reducing absolute heat exposure. This study underscores the importance of wetlands as a potential heat-mitigation factor against heat extremes and introduces a reproducible sensitivity framework for integrating wetland changes into regional climate modeling. The simulated cooling represents an upper-bound thermodynamic response to the prescribed wetland conditions rather than a direct estimate of the real-world magnitude of cooling caused by historical wetland loss. These findings highlight the potential role of preserving and restoring wetland ecosystems as part of climate adaptation strategies in heat-vulnerable regions.

This review examines advancements and methodologies in long-term wind-speed and -power forecasting. It emphasizes the importance of these techniques in integrating wind energy into power systems. Covering a range of forecasting timeframes from monthly to multiyear projections, this paper highlights the diversity of applications and approaches. These applications and approaches are essential for managing the inherent variability and unpredictability of wind energy. Various forecasting methods, including statistical models, machine-learning techniques, and hybrid models, are discussed in detail. The review demonstrates how these methods improve forecast accuracy and reliability across different temporal and geographical scales. It also identifies significant challenges such as model complexity, data limitations, and the need to accommodate regional variations. Future improvements in wind forecasting include enhancing model integration, employing higher resolution data, and fostering collaborative research to further refine forecasting methodologies. This comprehensive analysis aims to advance knowledge on wind forecasting, facilitate the efficient integration of wind power into global energy systems, and contribute to sustainable energy development goals.