Strategies for smarter catchment hydrology models: incorporating scaling and better process representation

Abstract Hydrological models have proliferated in the past several decades prompting debates on virtues and shortcomings of various modelling approaches. Rather than critiquing individual or approaches, objective here is to address critical issues scaling hydrological process representation types with suggestions for improving these attributes a parsimonious manner that captures explains their functionality as simply possible. This discussion focuses mostly conceptual physical/process-based where understanding internal catchment processes hydrologic pathways important. Such can be improved by using data from advanced remote sensing (both spatial temporal) derivatives, applications machine learning, flexible structures, informing through nested studies which are elucidated. Incorporating concepts connectivity into model structures promising approach flow path representation. Also important consideration scale dependency parameters avoid mismatch between measured modelled parameters. Examples presented high-elevation regions water sources differ temperate tropical environments more attention has been focused. The challenge incorporating spatially temporally variable inputs, hydrologically pathways, climate, land use requires modellers collaborate hydrologists include at relevant scales—i.e. develop smarter models.