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\begin{document}

\title{Learning from mistakes - Assessing the performance and uncertainty in
process-based models}



\author[1]{Moritz Feigl}%
\author[2]{Benjamin Roesky}%
\author[1]{Mathew Herrnegger}%
\author[1]{Karsten Schulz}%
\author[3]{Masaki Hayashi}%
\affil[1]{University of Natural Resources and Life Sciences Vienna}%
\affil[2]{BGC Engineering Inc}%
\affil[3]{University of Calgary}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\begin{abstract}
Typical applications of process- or physically-based models aim to gain
a better process understanding or provide the basis for a
decision-making process. To adequately represent the physical system,
models should include all essential processes. However, model errors can
still occur. Other than large systematic observation errors, simplified,
misrepresented, inadequately parametrized or missing processes are
potential sources of errors. This study presents a set of methods and a
proposed workflow for analyzing errors of process-based models as a
basis for relating them to process representations. The evaluated
approach consists of three steps: (i) training a machine learning (ml)
error-model using the input data of the process-based model and other
available variables, (ii) estimation of local explanations (i.e.,
contributions of each variable to a individual prediction) for each
predicted model error using SHapley Additive exPlanations (SHAP) in
combination with principal component analysis, (iii) clustering of SHAP
values of all predicted errors to derive groups with similar error
generation characteristics. By analyzing these groups of different
error-variable association, hypotheses on error generation and
corresponding processes can be formulated. That can ultimately lead to
improvements in process understanding and prediction. The approach is
applied to a process-based stream water temperature model HFLUX in a
case study for modelling an alpine stream in the Canadian Rocky
Mountains. By using available meteorological and hydrological variables
as inputs, the applied ml model is able to predict model residuals.
Clustering of SHAP values results in three distinct error groups that
are mainly related to shading and vegetation emitted longwave radiation.
Model errors are rarely random and often contain valuable information.
Assessing model error associations is ultimately a way of enhancing
trust in implemented processes and of providing information on potential
areas of improvement to the model.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Feigl_et_al_manuscript.docx`  available at \url{https://authorea.com/users/435642/articles/538521-learning-from-mistakes-assessing-the-performance-and-uncertainty-in-process-based-models}

\textbf{Hosted file}

\verb`Feigl_et_al_table01.docx`  available at \url{https://authorea.com/users/435642/articles/538521-learning-from-mistakes-assessing-the-performance-and-uncertainty-in-process-based-models}\selectlanguage{english}
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