The FRIEND/AMIGO focuses on advancing hydrosedimentological monitoring and modeling in river systems in Latin America and the Caribbean that are increasingly affected by climate variability and anthropogenic pressures. 

Our objective: use high-resolution Earth observation data to assess spatial and temporal variations in river connectivity and their implications for ecological resilience and water quality.

We integrate satellite altimetry (e.g., SWOT), UAV-based multispectral and hyperspectral imagery, and in-situ measurements such as ADCP and field spectrometry to characterize sediment transport processes, detect areas prone to erosion, and develop early warning indicators of morphodynamic changes. 

In parallel, we are exploring the use of emerging technologies and machine learning to support data-driven water resource management. This includes developing bio-optical models for monitoring eutrophication and acid mine drainage in vulnerable lakes and rivers, integrating hydrological models (such as QSWAT+) with satellite time series to estimate water balances, and defining environmental flow requirements using data sets from multiple sources. Our approach is based on a transdisciplinary and multiscale framework designed to support sustainable watershed management and climate adaptation strategies throughout the region.

Main research question :

1. How do sediment transport, river connectivity, and morphodynamic changes interact in river systems in Latin America and the Caribbean under scenarios of climate variability and anthropogenic pressure?
2. What combinations of remote sensing technologies (satellites and UAVs), in-situ data, and hydrological models can accurately estimate water quality, water balance, and environmental flows in data-scarce basins?
3. How can emerging technologies and artificial intelligence support the development of monitoring and early warning tools to enable adaptive water resource management at the basin scale?