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Adding machine learning to the MIP toolkit: Predictor importance for hydrological fluxes of Global Hydrological and Land Surface Models
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  • João Paulo Lyra Fialho Brêda,
  • Lieke Anna Melsen,
  • Ioannis Athanasiadis,
  • Albert vanDijk,
  • Vinícius Alencar Siqueira,
  • Anne Verhoef,
  • Yijian Zeng
João Paulo Lyra Fialho Brêda
Wageningen University & Research

Corresponding Author:[email protected]

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Lieke Anna Melsen
Wageningen University
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Ioannis Athanasiadis
Wageningen University & Research
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Albert vanDijk
ANU Centre for Water and Landscape Dynamics
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Vinícius Alencar Siqueira
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Anne Verhoef
University of Reading
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Yijian Zeng
Faculty of Geo-Information Science and Earth Observation, University of Twente
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Global Hydrological and Land Surface Models (GHM/LSMs) embody numerous interacting predictors and equations, complicating the diagnosis of primary hydrological relationships. We propose a model diagnostic approach based on Random Forest feature importance to detect the input variables that most influence simulated hydrological processes. We analyzed the JULES, ORCHIDEE, HTESSEL, SURFEX and PCR-GLOBWB models for the relative importance of precipitation, climate, soil, land cover and topographic slope as predictors of simulated average evaporation, runoff, and surface and subsurface runoffs. The machine learning model could reproduce GHM/LSMs outputs with a coefficient of determination over 0.85 in all cases and often considerably better. The GHM/LSMs agreed precipitation, climate and land cover share equal importance for evaporation prediction, and mean precipitation is the most important predictor of runoff. However, the GHM/LSMs disagreed on which features determine surface and subsurface runoff processes, especially with regards to the relative importance of soil texture and topographic slope.
28 Sep 2023Submitted to ESS Open Archive
28 Sep 2023Published in ESS Open Archive