Catchment characterization: Current descriptors, knowledge gaps and future opportunities

The ability to characterize hydrologically relevant differences between places is at the core of our science. A common way to quantitatively characterize hydrological catchments is through the use of descriptors that summarize relevant physical aspects of the system, typically by aggregating heterogeneous geospatial information into a single number. Such descriptors capture various facets of catchment functioning and structure, identify similarity or dissimilarity among catchments, and transfer hydrological information to unobserved locations. However, so far there is no agreement on how catchment descriptors should be selected, aggregated, and evaluated. Even worse, little is known about the existence of potential biases in the current practices to characterize catchments. In this systematic review, we analyze 742 research articles published between 1967 and 2021 to provide a categorized overview of current and historical practices of catchment characterization (i.e., data sources, aggregation and evaluation methods) in hydrological science and related disciplines. We uncover the existence of substantial biases in catchment characterization: (1) only 16% of the analyzed studies are in dry environments, even though such environments cover 42% of global land surface, suggesting that most catchment descriptors are tailored to represent energy-limited environments and are potentially less effective in water-limited environments; (2) only 30% of studies use subsurface features for catchment characterization despite the dominance of subsurface flow; (3) only 4% and 9% of descriptors are aggregated in spatially- and vertically-differentiated way, respectively, while the majority of descriptors are simple averages and do not account for hydrologically-relevant variabilities of features within catchments; (4) 25% of all studies analyzed do not evaluate the usefulness of catchment descriptors, and none quantifies their uncertainty. We demonstrate the potential effects of these biases on our ability to effectively characterize catchments and identify functional similarity of catchment behavior with illustrative examples. Finally, we suggest possible ways to derive more robust, comprehensive and hydrologically meaningful catchment descriptors.