The Philippines experiences intense rainfall but has limited ground-based monitoring infrastructure for flood prediction. Satellite rainfall products provide broad coverage but contain systematic biases that reduce operational usefulness. This study evaluated whether three correction methods—Quantile Mapping (QM), Random Forest (RF), and Hybrid Ensemble—maintain accuracy when applied to future periods with substantially different rainfall characteristics. Using the Cagayan de Oro River Basin in Northern Mindanao as a case study, models were trained on 2019–2020 data and tested on an independent 2021 period exhibiting 120% higher mean rainfall and 33% increased rainy-day frequency. During training, Random Forest and Hybrid Ensemble substantially outperformed Quantile Mapping (R² = 0.71 and 0.76 versus R² = 0.25 for QM). However, when tested under realistic operational constraints using seasonally incomplete calibration data (January–April only), performance rankings reversed completely. Quantile Mapping maintained operational reliability (R² = 0.53, RMSE = 5.23 mm), while Random Forest and Hybrid Ensemble failed dramatically (R² dropping to 0.46 and 0.41, respectively). This demonstrates that training accuracy poorly predicts operational reliability under changing rainfall regimes. Quantile Mapping’s percentile-based correction naturally adapts when rainfall patterns shift without requiring recalibration, while machine learning methods learned magnitude-specific patterns that failed when conditions changed. For flood early warning in data-limited basins with equipment failures and variable rainfall, only Quantile Mapping proved operationally reliable. This has practical implications for disaster risk reduction across the Philippines and similar tropical regions where standard validation approaches may systematically mislead model selection by measuring calibration performance rather than operational transferability.

Soil erosion affects agricultural and environmental sustainability and needs to be addressed. The Cagayan de Oro River Basin (CDORB), one of the major river basins in the Philippines, provides economic, social, and environmental services to the city and municipalities inside the basin. More than 70% of the area of the river basin is devoted to various forms of agricultural production. Land cover critically influences erosion dynamics as vegetation reduces rainfall impact, enhances infiltration, and limits sediment transport. This study employs the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) integrated with the Modified Universal Soil Loss Equation (MUSLE) to evaluate soil erosion under different rainfall return periods (5, 10, 25, 50, 100 years) and four land cover scenarios: No Reforestation Intervention (NI), Maximum Forest Cover (MF), Slope-Based Land Use (SB), and Reforestation on Public Domain (PD). Model results showed that soil loss increased with rainfall intensity, with NI yielding the highest average erosion of 1443 t ha⁻¹. Conservation scenarios reduced erosion by up to 53% compared to NI. Among the conservation scenarios, MF, SB, and PD yielded average erosion of 21, 716, and 1304 t ha⁻¹, respectively. While the MF scenario had the least soil loss, no space was assigned for economic production. On the other hand, the SB approach offered the best balance, halving erosion across all rainfall return periods, but at the same time has sufficient space available for economic production. These findings demonstrate the scientific value of integrating HEC-HMS and MUSLE for event-based erosion modeling and highlight how comparing multiple land-cover scenarios can inform data-driven land use planning and policy formulation for sustainable watershed management.

Urbanization has transformed natural landscapes, resulting in increased land surface temperatures and the intensification of urban heat island (UHI) effects. This study explores the relationship between land-use/land-cover (LULC) changes and land surface temperature (LST) from 2017 to 2024, using satellite data from Landsat and Sentinel. The results from supervised classification reveal a 50.9% increase in built-up land, from 21,256 hectares in 2017 to 32,099 hectares in 2024, accompanied by a 6.3% decline in woodland. Analysis of the LST data highlights rising temperatures in urbanized and deforested areas, with LST peaking at 36.96 °C in 2020 before slightly decreasing to 31.03 °C in 2024, potentially influenced by increased rainfall. However, hotspots of elevated LST persist, indicating sustained thermal stress. The urban thermal field variance index (UTFVI) showed worsening ecological conditions, particularly in densely urbanized zones. The study highlights the pressing need for integrating urban heat island (UHI) considerations into urban planning, as elevated urban temperatures threaten public health and escalate energy consumption. Additionally, the research aligns with Sustainable Development Goal 11 (SDG 11), emphasizing the creation of inclusive, safe, resilient, and sustainable cities. By providing policymakers with key UHI indices, this study contributes to climate-resilient urban environments, mitigating heat risks through green infrastructure and sustainable urban design.

Urbanization has transformed natural landscapes, resulting in increased land surface temperatures and the intensification of Urban Heat Island (UHI) effects. This study explores the relationship between land use/land cover (LULC) changes and land surface temperature (LST) from 2017 to 2024, using satellite data from Landsat and Sentinel. Results from supervised classification reveal a 50.9% increase in built-up land, from 21,256 hectares in 2017 to 32,099 hectares in 2024, accompanied by a 6.3% decline in woodland. Analysis of LST data highlights rising temperatures in urbanized and deforested areas, with LST peaking at 36.96 °C in 2020 before slightly decreasing to 31.03 °C in 2024, potentially influenced by increased rainfall. However, hotspots of elevated LST persist, indicating sustained thermal stress. The Urban Thermal Field Variance Index (UTFVI) showed worsening ecological conditions, particularly in densely urbanized zones. The study highlights the pressing need for integrating Urban Heat Island (UHI) considerations into urban planning, as elevated urban temperatures threaten public health and escalated energy consumption. Additionally, the research aligns with Sustainable Development Goal 11 (SDG 11), emphasizing the creation of inclusive, safe, resilient, and sustainable cities. By providing policymakers with key UHI indices, this study contributes to climate-resilient urban environments, mitigating heat risks through green infrastructure and sustainable urban design.