The role of tracers in hydrological process understanding: a global perspective

To manage water resources and forecast river flows, hydrologists seek to understand the processes that move water from precipitation, through watersheds, into river channels. A fundamental question in hydrology is to explain where and when different processes occur, and how they are controlled by climate and landscape. Understanding the patterns and drivers of hydrological processes over large scales will transform our ability to build accurate forecast models, to improve water management based on knowledge of water storage and flow, and to adapt to changes in hydrological extremes.

Much of our understanding of runoff generation processes and their drivers derives from research watersheds, where intensive monitoring and analysis allows hydrologists to develop a detailed perceptual model of water sources, flow paths, and residence times. This study quantifies the role of tracer data in process understanding, by synthesizing knowledge from a global database of 400 research watersheds with published descriptions of dominant flow pathways. By examining the underlying journal articles in the database, we assess what types of field evidence are most valuable to deduce dominant flow pathways and evaluate the strength of evidence in individual watersheds. Using a standard process classification, we analyze the extent to which tracer data has proved effective for understanding a range of hydrological processes across different climate and landscape regions.

This study shows how a knowledge synthesis approach enables deep investigation into how hydrologists use tracers to generate knowledge about runoff generation processes, leveraging decades of grant funding and fieldwork effort. Our results will be valuable for the design of future hydrological observatories or research watersheds that seek to analyze dominant runoff generation processes for applications such as flood mitigation and watershed restoration.