How to Tailor my Process-based Hydrological Model? Dynamic Identifiability Analysis of Flexible Model Structures

Axel Bronstert,Tobias Pilz,Gabriele Baroni,Till Francke

doi:10.5194/egusphere-egu2020-20280

Axel Bronstert, Tobias Pilz + Show 2 more

Open Access

https://doi.org/10.5194/egusphere-egu2020-20280

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Publication Date: Mar 23, 2020

License type: cc-by

Affiliation: University of Potsdam, University of Bologna

Abstract

&lt;p&gt;In the field of hydrological modeling, many alternative mathematical representations of natural processes exist. To choose specific process formulations when building a hydrological model is therefore associated with a high degree of ambiguity and subjectivity. Identifiability analysis may provide guidance by constraining the a priori range of alternatives based on observations. In this work, a flexible simulation environment is used to build a process-based hydrological model with alternative process representations, numerical integration schemes, and model parametrizations in an integrated manner. The flexible simulation environment is coupled with an approach for dynamic identifiability analysis. The objective is to investigate the applicability of the coupled framework to identify the most adequate model structure. It turned out that identifiability of model structure varies in space and time, driven by the meteorological and hydrological characteristics of the study area. Moreover, the most accurate numerical solver is often not the best performing solution. This is possibly influenced by correlation and compensation effects among process representation, numerical solver, and parametrization. Overall, the proposed coupled framework proved to be applicable for the identification of adequate process-based model structures and is therefore a useful diagnostic tool for model building and hypotheses testing.&lt;/p&gt;

Highlights

Computer models are imperfect abstractions and simplifications of the real world transferred into computer code
We extend the use of the Monte Carlo (MC) filtering approach as implemented in the dynamic identifiability analysis (DYNIA) framework by Wagener et al (2003) to account for model structures, and we explore its capability for model building
Model Errors To carry out the experiments with the different model structures and parametrizations, the ecohydrological simulation environment (ECHSE) environment was run 12,000 times according to the sampled model configurations

Summary

Introduction

Computer models are imperfect abstractions and simplifications of the real world transferred into computer code As such, they necessarily impose uncertainties when simulating a certain process or the evolution of a variable of interest. The diversity of landscapes, data sets, and specific research objectives led to the development of a large number of different hydrological models. These can vary in their conceptualization, how and to which degree of realism hydrological processes are represented, the discretization of a landscape into spatial model units, model runtime, initialization efforts, the number of parameters, whether parameters should be calibrated or not, and under which environmental conditions they are appropriate simulation tools (e.g., Clark et al, 2011; Fenicia et al, 2016; Weiler & Beven, 2015). Model development is problem or even catchment specific, where no straightforward solutions exist and compromises need to be made

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