Current practices in catchment characterization: data sources, aggregation approaches, derived descriptors and their value

A common way to characterize catchments is to use catchment descriptors that summarize important physical aspects of a given catchment, often by aggregating large geospatial datasets into a single number. Such descriptors aim at extracting information that can inform us about different aspects of catchment functioning, help us to infer dominant hydrological processes, identify similarity among different sites, and transfer information across them. In this study we analyze a large sample of research articles indexed in Scopus, that were returned from the search words “catchment characteristic”, “catchment descriptor”, “catchment attribute”, “catchment indicator” or “catchment property”, to identify current practices of catchment characterization in hydrological science and related disciplines.

We particularly focus on analyzing the variety of data sources on which catchment descriptors are usually based (e.g., digital elevation, land use, lithographic and soil texture maps), on identifying how the datasets are aggregated into catchment descriptors, and on exploring how the value of those descriptors is assessed.

Based on this large sample of studies that cover diverse research areas (e.g., water quantity, water quality, lake research, aquatic ecosystems), different types of studies (e.g., data-based analysis, hydrological and statistical modeling, field studies) and various application purposes (e.g., descriptive site comparison, catchment clustering/classification, quantitative driver/control identification, regionalization), we provide a categorized overview of practices that are currently used for catchment characterization. This overview will provide guidance for future studies by summarizing the status quo and its strengths and limitations, and by providing suggestions for future research.