Understanding and quantifying the coupled effects of land cover change and climate change on hydrological regimes is critical for sustainable water management in tropical mountainous regions. The Río Negro Basin in eastern Antioquia, Colombia, has undergone rapid urban expansion, agricultural intensification, and deforestation over recent decades, profoundly altering its hydrological dynamics. This study integrates advanced satellite image processing, AI-based land cover modeling, climate change projections, and distributed hydrological simulation to assess future streamflow responses. Multi-sensor satellite data (Landsat, Sentinel-1, Sentinel-2, ALOS) were processed using Random Forest classifiers, intelligent multisensor fusion, and probabilistic neural networks to generate high-resolution land cover maps and scenarios for 2060 (optimistic, trend, and pessimistic), with strict area constraints for urban growth and forest conservation. Future precipitation was derived from MPI-ESM CMIP6 outputs (SSP2-4.5, SSP3-7.0, SSP5-8.5) and statistically downscaled using Empirical Quantile Mapping (EQM) to match the basin scale and precipitation records from the national hydrometeorological service of the Colombia IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales, Colombia). The TETIS hydrological model was calibrated and validated using observed streamflow records (1998–2023) and subsequently used to simulate hydrological responses under combined land cover and climate scenarios. Results indicate that urban expansion and forest loss significantly increase peak flows (Q90, Q95) and flood risk while decreasing baseflows (Q10, Q30), compromising water availability during dry seasons. Conversely, conservation-oriented scenarios mitigate these effects by enhancing flow regulation and groundwater recharge. The findings highlight that targeted land management can partially offset the negative impacts of climate change, underscoring the importance of integrated land–water planning in the Andes. This work provides a replicable framework for modeling hydrological futures in data-scarce mountainous basins, offering actionable insights for regional authorities, environmental agencies, and national institutions responsible for water security and disaster risk management.

This study presents a novel methodology for the detection and monitoring of changes in surface water bodies, with a particular emphasis on the near-eastern region of Antioquia, Colombia. The proposed approach integrates remote sensing and artificial intelligence techniques through the fusion of multi-source imagery, specifically Synthetic Aperture Radar (SAR) and optical data. The framework is structured in several stages. First, radar imagery is pre-processed using an autoencoder-based despeckling model, which leverages deep learning to reduce noise while preserving structural information critical for environmental monitoring. Concurrently, optical imagery is processed through the computation of normalized spectral indices, including NDVI, NDWI, and NDBI, capturing essential characteristics related to vegetation, water presence, and surrounding built-up areas. These complementary sources are subsequently fused into synthetic RGB composite representations, ensuring spatial and spectral consistency between radar and optical domains. To operationalize this methodology, a standardized and reproducible workflow was implemented for automated image acquisition, preprocessing, fusion, and segmentation. The Segment Anything Model (SAM) was integrated into the process to generate semantically interpretable classes, enabling more precise delineation of hydrological features, flood-prone areas, and urban expansion near waterways. This automated system was embedded in a software prototype, allowing local users to manage large volumes of satellite data efficiently and consistently. The results demonstrate that the combination of SAR and optical datasets provides a robust solution for monitoring dynamic hydrological environments, particularly in tropical mountainous regions with persistent cloud cover. The fused products enhanced the detection of small streams and complex hydrological patterns that are typically challenging to monitor using optical imagery alone. By integrating these technical advancements, the methodology supports improved environmental monitoring and provides actionable insights for decision-makers. At the local scale, municipal governments can use these outputs for urban planning and flood risk mitigation; at the regional level, environmental and territorial authorities can strengthen water resource management and conservation strategies; and at the national level, risk management institutions can incorporate this information into early warning systems and disaster preparedness programs. Overall, this research delivers a scalable and automated tool for surface water monitoring, bridging the gap between scientific innovation and operational decision-making to support sustainable watershed management under increasing pressures from climate change and urbanization.