Synthesizing Time-Variance in Catchment Hydrologic Function: Patterns, Controls, Methodological Advances and Global Extrapolation to Ungauged Basins

Time-variance in catchment hydrologic function—how rainfall-runoff relationships shift across events, seasons, and years—remains a fundamental yet incompletely understood aspect of catchment behavior. Synthesizing this time-variance across scales is essential for advancing hydrological theory, improving predictions in ungauged basins, and guiding model development. Here, I present a synthesis of recent work spanning local to continental to global scales, integrating fine-resolution mechanistic models with large-sample data-driven methods.At the local scale, process-based modeling reveals how subsurface physical structure—including subsurface lateral permeability pattern—mediates the climate-induced time-varying partitioning of water between long-term storage, shallow and deep flow paths, and evapotranspiration. At continental to global scales, large-sample analyses across more than five thousand gauged catchments and 80,000 ungauged catchments expose systematic patterns in functional complexity (or time-variance): most catchments exhibit strongly time-varying rainfall-runoff behavior, with climate (particularly rainfall persistence and aridity) providing the dominant control, while geology and topography modulate outcomes locally.To enable these syntheses, we developed new data-driven methodologies for extracting catchment hydrologic function and quantifying its temporal variation from observational records. These methods provide a transferable framework for diagnosing functional behavior in gauged systems. These findings advance process-based explanations of hydrological phenomena across places and scales, connect event-scale dynamics to seasonal and long-term patterns, and offer new tools for identifying hydrological signatures in data. The implications extend to model structure selection, monitoring network design, and the development of a unifying hydrological theory that accommodates—rather than assumes away—functional time-variance.