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    <titleInfo>
        <title>On the optimal level of complexity for the representation of wetland systems in land surface models</title>
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    <name type="personal">
        <namePart type="given">Mennatullah</namePart>
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        <namePart type="family">Ireson</namePart>
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    <name type="personal">
        <namePart type="given">Saman</namePart>
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    <abstract>Abstract. Wetland systems are among the largest stores of carbon on the planet, most biologically diverse of all ecosystems, and dominant controls of the hydrologic cycle. However, their representation in land surface models (LSMs), which are the terrestrial lower boundary of Earth system models (ESMs) that inform climate actions, is limited. Here, we explore different possible parametrizations to represent wetland-groundwater-upland interactions with varying levels of system and computational complexity. We perform a series of numerical experiments that are informed by field observations from wetlands in the well-instrumented White Gull Creek in Saskatchewan, in the boreal region of North America. We show that the typical representation of wetlands in LSMs, which ignores interactions with groundwater and uplands, can be inadequate. We show that the optimal level of model complexity depends on the land cover, soil type, and the ultimate modelling purpose, being nowcasting and prediction, scenario analysis, or diagnostic learning.</abstract>
    <identifier type="citekey">Elrashidy-2023-On</identifier>
    <identifier type="doi">10.5194/hess-2023-68</identifier>
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        <date>2023</date>
        <detail type="volume"><number>2023</number></detail>
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%0 Journal Article
%T On the optimal level of complexity for the representation of wetland systems in land surface models
%A Elrashidy, Mennatullah
%A Ireson, Andrew
%A Razavi, Saman
%J Hydrology and Earth System Sciences Discussions, Volume 2023
%D 2023
%V 2023
%I Copernicus GmbH
%F Elrashidy-2023-On
%X Abstract. Wetland systems are among the largest stores of carbon on the planet, most biologically diverse of all ecosystems, and dominant controls of the hydrologic cycle. However, their representation in land surface models (LSMs), which are the terrestrial lower boundary of Earth system models (ESMs) that inform climate actions, is limited. Here, we explore different possible parametrizations to represent wetland-groundwater-upland interactions with varying levels of system and computational complexity. We perform a series of numerical experiments that are informed by field observations from wetlands in the well-instrumented White Gull Creek in Saskatchewan, in the boreal region of North America. We show that the typical representation of wetlands in LSMs, which ignores interactions with groundwater and uplands, can be inadequate. We show that the optimal level of model complexity depends on the land cover, soil type, and the ultimate modelling purpose, being nowcasting and prediction, scenario analysis, or diagnostic learning.
%R 10.5194/hess-2023-68
%U https://gwf-uwaterloo.github.io/gwf-publications/G23-2003
%U https://doi.org/10.5194/hess-2023-68

Markdown (Informal)

[On the optimal level of complexity for the representation of wetland systems in land surface models](https://gwf-uwaterloo.github.io/gwf-publications/G23-2003) (Elrashidy et al., GWF 2023)

• On the optimal level of complexity for the representation of wetland systems in land surface models (Elrashidy et al., GWF 2023)

ACL

• Mennatullah Elrashidy, Andrew Ireson, Saman Razavi, Mennatullah Elrashidy, Andrew Ireson, and Saman Razavi. 2023. On the optimal level of complexity for the representation of wetland systems in land surface models. Hydrology and Earth System Sciences Discussions, Volume 2023, 2023.