A global inventory of streamflow and springflow events and their generation processes

Streamflow and springflow events contain essential information on how catchments store and release water as a response to incoming precipitation. When evaluated together with the corresponding hydro-meteorological event conditions, they can reveal the dominant runoff generation processes in a catchment. However, there is currently no consistent, objective approach for identifying and assessing such events on a global scale. To provide an open-access, global inventory of events and their generation processes to the hydrological community, we first employ an objective automated event identification method (Giani et al., 2022) and adapt the algorithm to different climatic conditions. For each of the identified events, we calculate event-scale hydrometric signatures (e.g., event runoff coefficients). In a second step, we classify all identified events based on their hydro-meteorological conditions (e.g., snowmelt, intensive rainfall). To do this, we set up a deep learning model for each catchment and predict streamflow events using observed hydro-meteorological information (precipitation and temperature) and global simulations of a hydrological model for soil moisture and snowmelt. We use explainable machine learning to reveal the importance of each of the predictors during the event build up period and to infer the corresponding generation process for each of the identified events (e.g., snowmelt-induced event, rainfall-induced event during wet antecedent conditions). The global inventory of streamflow and springflow events can provide useful process-oriented information based on event-scale signatures for the evaluation of large-scale hydrological models. Furthermore, it potentially serves as a basis for a more effective parameter regionalization based on similarity of dominant hydro-meteorological event conditions across different locations.

Giani, G., Tarasova, L., Woods, R. A., & Rico‐Ramirez, M. A. (2022). An objective time‐series‐analysis method for rainfall‐runoff event identification. Water Resources Research, 58(2), e2021WR031283. https://doi.org/10.1029/2021WR031283