Relationship Between Calibration Time and Final Performance of Conceptual Rainfall-Runoff Models

Abstract

Various methods are used in the literature for calibration of conceptual rainfall-runoff models. However, very rarely the question on the relation between the number of model runs (or function calls) and the quality of solutions found is asked. In this study two lumped conceptual rainfall-runoff models (HBV and GR4J with added snow module) are calibrated for five catchments, located in temperate climate zones of USA and Poland, by means of three modern variants of Evolutionary Computation and Swarm Intelligence optimization algorithms with four different maximum numbers of function calls set to 1000, 3000, 10,000 and 30,000. At the calibration stage, when more than 10,000 function calls is used, only marginal improvement in model performance has been found, irrespective of the catchment or calibration algorithm. For validation data, the relation between the number of function calls and model performance is even weaker, in some cases the longer calibration, the poorer modelling performance. It is also shown that the opinion on the model performance based on different popular hydrological criteria, like the Nash-Sutcliffe coefficient or the Persistence Index, may be misleading. This is because very similar, largely positive values of Nash-Sutcliffe coefficient obtained on different catchments may be accompanied by contradictory values of the Persistence Index.

Optimization of the Multi-Start Strategy of a Direct-Search Algorithm for the Calibration of Rainfall–Runoff Models for Water-Resource Assessment

“…We joined the conclusions of studies of [6,16,17,19], in which it was argued that algorithms catalogued as traditional or historical, could work efficiently to solve actual problems if they are used correctly. The evaluation of the behaviour of a local-search algorithm improved with the random sampling method, in the calibration of the main rainfall-runoff models used in the WRA, was considered.…”

Water and carbon balances in a hemi-boreal forest

“…The quality of the calibration was measured by the Nash-Sutcliffe Efficiency (NSE). Monte-Carlo-Simulation was used to perform the calibration of the GR4J-Cemaneige model constants (Piotrowski et al, 2019;McIntyre et al, 2002;Vrugt et al, 2008). The Nash Sutcliffe model efficiency coefficient (NSE) was used to assess the predictive skill of the GR4J-Cemaneige hydrological model.…”

Decentralized calibration process for distributed water resources systems using the self-adaptive multi-memory melody search algorithm

“…Although these algorithms do not guarantee a global optimum (Li et al 2016), they have multiple advantages such as ease of programming and application, ease of linking to simulation models, controllable searching process, and the use of efficient search operators to reduce the computational volume. Moreover, the efficiency and performance of metaheuristic algorithms in solving calibration problems are dependent on the determination of algorithm parameters in addition to the structure of algorithm operators (Piotrowski et al 2019). This can be considered as strength but considerably increases the computational volume and thereby costs because before applying metaheuristic optimizer algorithms, the optimal values of their parameters can be determined via an extensive sensitivity analysis with high computational costs.…”