Preprints
https://doi.org/10.5194/hess-2023-304
https://doi.org/10.5194/hess-2023-304
30 Apr 2024
 | 30 Apr 2024
Status: this preprint is currently under review for the journal HESS.

Implementation of global soil databases in NOAH-MP model and the effects on simulated mean and extreme soil hydrothermal changes

Kazeem Ishola, Gerald Mills, Ankur Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy

Abstract. Soil properties and their associated hydro-physical parameters represent a significant source of uncertainty in Land Surface Models (LSMs) with consequent effects on simulated sub-surface thermal and moisture characteristics, surface energy exchanges and turbulent fluxes. These effects can result in large model differences particularly during extreme events. Typical of many model based approaches, spatial soil information such as location, extent and depth of textural classes are derived from coarse scale soil information and employed largely due to their ready availability rather than suitability. However, the use of a particular spatial soil dataset has important consequences for many of the processes simulated within a LSM. This study investigates NOAH-MP model uncertainty in simulating soil moisture (expressed as a ratio of water to soil volume, m3 m-3) and soil temperature changes associated with two widely used global soil databases (STATSGO and SOILGRIDS) across the Island of Ireland. Both soil datasets produced a significant dry bias in loam soils, up to 0.15 m3 m-3 in a wet period and 0.10 m3 m-3 in a dry period. The spatial disparities between STATSGO and SOILGRIDS also influenced the regional soil hydrothermal changes and extremes. SOILGRIDS was found to intensify drought characteristics – shifting low/moderate drought areas into extreme/exceptional during dry periods – relative to STATSGO. Our results demonstrate that the coarse STATSGO performs as good as the fine-scale SOILGRIDS soil database. However, the results underscore the need to develop detailed regionally-derived soil texture characteristics, and for better representations of soil physics in LSMs to improve operational modeling and forecasting of hydrological processes and extremes.

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Kazeem Ishola, Gerald Mills, Ankur Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy

Status: open (until 25 Jun 2024)

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Kazeem Ishola, Gerald Mills, Ankur Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy
Kazeem Ishola, Gerald Mills, Ankur Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy

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Short summary
The global soil information contributes to uncertainty in many models that monitor soil hydrothermal changes. Using the NOAH-MP model with two different global soil information, we show under-represented soil properties in wet loam soil, leading to dry bias in soil moisture. The dry bias is higher and drought categories are more severe in SOILGRIDS. We conclude that models should consider using detailed regionally-derived soil information, to reduce model uncertainties.