Urban flooding represents a growing concern on a global scale, particularly in regions characterized by rapid urbanization and increased climate variability. This study concentrates on the Rangsit area in Pathum Thani Province, Thailand, an urbanizing peri-urban area north of Bangkok and within the Chao Phraya River Basin where transitions in land use and the intensification of rainfall induced by climate change are elevating flood risks. A physically based hydrodynamic model was developed utilizing PCSWMM to assess current and future flood scenarios that considered future build-out plans and climate change scenarios. The model underwent calibration and validation using a continuous modeling approach that conservatively focused on wet year conditions, based on available rainfall and water level data. In assessing future scenarios, we considered land use projections based on regional development plans and climate projections downscaled under RCP4.5 and RCP8.5 pathways. Results indicate that both urban expansion and intensifying rainfall are likely to increase flood magnitudes, durations, and impacted areas, although in this rapidly developing peri-urban area, land use change was the most important driver. The findings suggest that a physically based modeling approach could support a smart-control framework that could effectively inform evidence-based urban planning and infrastructure investments. These insights are of paramount importance for flood-prone regions in Thailand and Southeast Asia, where dynamic modeling tools must underpin governance, climate adaptation, and risk communication. Furthermore, given the greater impact of future build-out on flood risk, as compared to climate change, there is an opportunity to effectively and proactively improve flood resilience through the implementation of integrated Nature-based Solution and hard engineering approaches, in combination with effective flood management policy.

Biochemical Oxygen Demand (BOD) is a key indicator of organic pollution and a proxy indicator reflecting organic loading that can indirectly influence eutrophication processes in aquatic systems. This study presents a spatially explicit, physically based modeling framework for simulating BOD dynamics in the urbanizing Chao Phraya and Tha Chin Rivers Basin in central Thailand. The framework integrates the Personal Computer Storm Water Management Model (PCSWMM) with GIS-based datasets to represent pollutant sources, hydraulic flow, and land use. The model was calibrated and validated using data from 36 monitoring stations (2021–2022), achieving strong performance: an NSE of 0.72 and an MAE of 0.35 mg/L for the Chao Phraya River, and an NSE of 0.88 and an MAE of 0.12 mg/L for the Tha Chin River. Scenario simulations for 2032 projected BOD concentrations exceeded 4 mg/L in several downstream segments under the baseline (no-intervention) scenario, indicating elevated organic pollution and potential oxygen depletion that may indirectly exacerbate eutrophication risk in the Upper Gulf of Thailand, particularly in tidal zones with low dilution and nutrient accumulation. Model projections suggest that effective mitigation would require a 20–30% reduction in BOD loads, achievable through enhanced wastewater treatment and stricter pollution controls. Although BOD reduction alone cannot eliminate eutrophication, it supports broader nutrient management efforts by improving baseline water quality conditions. The proposed model offers a robust tool for identifying pollution hotspots, evaluating management strategies, and informing integrated river basin policies under continued urban growth.