Changing-CAMELS: A pan-European dataset for non-stationary hydrological modeling under climate and land-use change

Non-stationarity has long been recognized as a fundamental challenge in hydrological modelling, as climate change and human activities continuously alter catchment properties and the boundary conditions under which hydrological systems operate. However, translating this long-standing recognition into systematic model evaluation remains challenging, as suitable large-sample hydrological datasets that explicitly represent temporal change are still scarce. Most existing datasets adopt a static design, with time-invariant catchment attributes and hydro-meteorological time series limited to retrospective observations, which constrains the systematic testing of hypotheses and models targeting non-stationary hydrological behaviour. Here, we introduce Changing-CAMELS, a pan-European, CAMELS-style dataset explicitly designed to support non-stationary hydrological modelling. Building on the strengths of existing datasets such as CAMELS, Caravan, and EStreams, the developed dataset moves beyond static representations by incorporating time-varying catchment attributes and future climate forcing on a European scale.

In particular, dynamic land-use and land-cover changes are derived from the European LUCAS dataset, providing annual updates to vegetation and land-cover fractions. Simultaneously, the inclusion of both raw and bias-corrected daily-resolution regional and global climate model datasets extends hydro-meteorological forcing beyond the historical period. This integration enables consistent analyses of evolving land cover, shifting climate regimes, and their combined impacts on hydrological responses. Changing-CAMELS covers 4,575 catchments across Europe and harmonizes observations and attributes across national boundaries. By providing both retrospective and prospective information within a unified framework, the dataset allows researchers to systematically evaluate competing hypotheses, compare stationary and non-stationary model formulations, and assess the robustness and uncertainty of hydrological models under climate and land-use change.