Automatic Model Structure Identification for Conceptual Hydrologic Models

Abstract

AbstractChoosing (an) adequate model structure(s) for a given purpose, catchment, and data situation is a critical task in the modeling chain. However, despite model intercomparison studies, hypothesis testing approaches with modular modeling frameworks, and continuous efforts in model development and improvement, there are still no clear guidelines for identifying a preferred model structure. By introducing a framework for Automatic Model Structure Identification (AMSI), we support the process of identifying (a) suitable model structure(s) for a given task. The proposed AMSI framework employs a combination of the modular hydrological model RAVEN and the heuristic global optimization algorithm dynamically dimensioned search (DDS). It is the first demonstration of a mixed‐integer optimization algorithm applied to simultaneously optimize model structure choices (integer decision variables) and parameter values (continuous decision variables) in hydrological modeling. The AMSI framework is thus able to sift through a vast number of model structure and parameter choices for identifying the most adequate model structure(s) for representing the rainfall‐runoff behavior of a catchment. We demonstrate the feasibility of the approach by reidentifying given model structures that produced a specific hydrograph and show the limits of the current setup via a real‐world application of AMSI on 12 MOPEX catchments. Results show that the AMSI framework is capable of inferring feasible model structure(s) reproducing the rainfall‐runoff behavior of a given catchment. However, it is a complex optimization problem to identify model structure and parameters simultaneously. The variance in the identified structures is high due to near equivalent diagnostic measures for multiple model structures, reflecting substantial model equifinality. Future work with AMSI should consider the use of hydrologic signatures, case studies with multiple types of observation data, and the use of mixed‐integer multiobjective optimization algorithms.