The Great Catchment Hysteresis Challenge

Abstract

This paper makes the argument for treating the response of small catchments as an exercise in the identification of hysteretic functions as a way of overcoming the impossibility of knowing all the small-scale detail of time variable and spatial heterogeneous catchment processes. An initial form of analysis is proposed based on the Data-Based Mechanistic (DBM) transfer function methodology to define functions for classes of events based on rainfall input volumes and antecedent flow as an index of catchment wetness. From the resulting transfer functions, event-scale storage-discharge plots can be derived if within-event evapotranspiration is neglected as small relative to event inputs. The great hysteresis challenge is to find a way of identifying a more continuous mechanistic function that allows for the change in input–output gains and transfer function state variables directly from the observed data without invoking a particular conceptual storage model structure (or structures) or a difficult-to-interpret machine-learning framework.