<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="Mai-2023-Ten">
    <titleInfo>
        <title>Ten strategies towards successful calibration of environmental models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Juliane</namePart>
        <namePart type="family">Mai</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Journal of Hydrology, Volume 620</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>Elsevier BV</publisher>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>Model calibration is the procedure of finding model settings such that simulated model outputs best match the observed data. Model calibration is necessary when the model parameters cannot directly be measured as is the case with a wide range of environmental models where parameters are conceptually describing upscaled and effective physical processes. Model calibration is therefore an important step of environmental modeling as the model might otherwise provide random outputs if never compared to a ground truth. Model calibration itself is often referred to be an art due to its plenitude of intertwined steps and necessary decisions along the way before a calibration can be carried out or can be regarded successful. This work provides a general guide specifying which steps a modeler needs to undertake, how to diagnose the success of each step, and how to identify the right action to revise steps that were not successful. The procedure is formalized into ten iterative steps generally appearing in calibration experiments. Each step of this “calibration life cycle” is either illustrated with an exemplary calibration experiment or providing an explicit checklist the modeler can follow. These ten strategies are: (1) using sensitivity information to guide the calibration, (2) handling of parameters with constraints, (3) handling of data ranging orders of magnitude, (4) choosing the data to base the calibration on, (5) presenting various methods to sample model parameters, (6) finding appropriate parameter ranges, (7) choosing objective functions, (8) selecting a calibration algorithm, (9) determining the success and quality of a multi-objective calibration, and (10) providing a checklist to diagnose calibration performance using ideas introduced in the previous steps. The formal definition of strategies through the calibration process is providing an overview while shedding a light on connections between these main ingredients to calibrate an environmental model and will therefore enable especially novice modelers to succeed.</abstract>
    <identifier type="citekey">Mai-2023-Ten</identifier>
    <identifier type="doi">10.1016/j.jhydrol.2023.129414</identifier>
    <location>
        <url>https://gwf-uwaterloo.github.io/gwf-publications/G23-10001</url>
    </location>
    <part>
        <date>2023</date>
        <detail type="volume"><number>620</number></detail>
        <detail type="page"><number>129414</number></detail>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Ten strategies towards successful calibration of environmental models
%A Mai, Juliane
%J Journal of Hydrology, Volume 620
%D 2023
%V 620
%I Elsevier BV
%F Mai-2023-Ten
%X Model calibration is the procedure of finding model settings such that simulated model outputs best match the observed data. Model calibration is necessary when the model parameters cannot directly be measured as is the case with a wide range of environmental models where parameters are conceptually describing upscaled and effective physical processes. Model calibration is therefore an important step of environmental modeling as the model might otherwise provide random outputs if never compared to a ground truth. Model calibration itself is often referred to be an art due to its plenitude of intertwined steps and necessary decisions along the way before a calibration can be carried out or can be regarded successful. This work provides a general guide specifying which steps a modeler needs to undertake, how to diagnose the success of each step, and how to identify the right action to revise steps that were not successful. The procedure is formalized into ten iterative steps generally appearing in calibration experiments. Each step of this “calibration life cycle” is either illustrated with an exemplary calibration experiment or providing an explicit checklist the modeler can follow. These ten strategies are: (1) using sensitivity information to guide the calibration, (2) handling of parameters with constraints, (3) handling of data ranging orders of magnitude, (4) choosing the data to base the calibration on, (5) presenting various methods to sample model parameters, (6) finding appropriate parameter ranges, (7) choosing objective functions, (8) selecting a calibration algorithm, (9) determining the success and quality of a multi-objective calibration, and (10) providing a checklist to diagnose calibration performance using ideas introduced in the previous steps. The formal definition of strategies through the calibration process is providing an overview while shedding a light on connections between these main ingredients to calibrate an environmental model and will therefore enable especially novice modelers to succeed.
%R 10.1016/j.jhydrol.2023.129414
%U https://gwf-uwaterloo.github.io/gwf-publications/G23-10001
%U https://doi.org/10.1016/j.jhydrol.2023.129414
%P 129414

Markdown (Informal)

[Ten strategies towards successful calibration of environmental models](https://gwf-uwaterloo.github.io/gwf-publications/G23-10001) (Mai, GWF 2023)

• Ten strategies towards successful calibration of environmental models (Mai, GWF 2023)

ACL

• Juliane Mai. 2023. Ten strategies towards successful calibration of environmental models. Journal of Hydrology, Volume 620, 620:129414.