Are modern metaheuristics successful in calibrating simple conceptual rainfall–runoff models?. Issue 4 (12th March 2017)

• Record Type: Journal Article
• Authors: Piotrowski, Adam P.; Napiorkowski, Maciej J.; Napiorkowski, Jaroslaw J.; Osuch, Marzena; Kundzewicz, Zbigniew W.
• Is Part Of: Hydrological sciences journal. Volume 62:Issue 4(2017)
• Page Start: 606
• Page End: 625
• Publication Date: 2017-03-12
• Subjects: conceptual rainfall–runoff models -- HBV -- GR4J -- evolutionary algorithms -- differential evolution -- particle swarm optimization; Hydrology -- Periodicals; 551.4805
• DOI: 10.1080/02626667.2016.1234712
• Languages: English
• ISSNs: 0262-6667

Abstract

ABSTRACT: In recent years sampling approaches have been used more widely than optimization algorithms to find parameters of conceptual rainfall–runoff models, but the difficulty of calibration of such models remains in dispute. The problem of finding a set of optimal parameters for conceptual rainfall–runoff models is interpreted differently in various studies, ranging from simple to relatively complex and difficult. In many papers, it is claimed that novel calibration approaches, so-called metaheuristics, outperform the older ones when applied to this task, but contradictory opinions are also plentiful. The present study aims at calibration of two simple lumped conceptual hydrological models, HBV and GR4J, by means of a large number of metaheuristic algorithms. The tests are performed on four catchments located in regions with relatively similar climatic conditions, but on different continents. The comparison shows that, although parameters found may somehow differ, the performance criteria achieved with simple lumped models calibrated by various metaheuristics are very similar and differences are insignificant from the hydrological point of view. However, occasionally some algorithms find slightly better solutions than those found by the vast majority of methods. This means that the problem of calibration of simple lumped HBV or GR4J models may be deceptive from the optimization perspective, as the vast majority of algorithms that follow a common evolutionary principle of survival of the fittest lead to sub-optimal solutions.