We analysed 20 years of satellite data to study changes in surface phytoplankton concentration in Lake Tanganyika. Results reveal strong seasonal patterns linked to known hydrodynamics, with decreasing levels in deep areas and increases in shallow regions. These changes could alter the lake’s ecosystem and threaten the resources that millions of people rely on.

This study takes a data-driven approach to evaluate long-term groundwater nitrate concentration trends in Wallonia, Belgium, over ~20 years following the implementation of regional nitrogen management policies. Results highlight a persistently negative impact of agricultural land use, the importance of sustained policies and long-term monitoring to mitigate nitrogen legacy effects, and the need for improved datasets to better capture controlling factors. 

Causal inference methods (CIMs) aim at identifying causal links from temporal dependencies found in time-series data. Using both synthetic data and real-time series from a karst system, we study and discuss the potential of four CIMs to reveal hydrological connections between variables in hydrological systems. Despite the ever-present risk of spurious hydrological connections, our results highlight that the nonlinear and multivariate CIM has a substantially lower false-positive rate.