Simple process-led algorithms for simulating habitats (SPLASH v 2.0): robust calculations of water and energy fluxes
File(s)
Published version
Author(s)
Sandoval Calle, David
Prentice, Iain Colin
Nobrega, Rodolfo
Type
Journal Article
Abstract
The current representation of key processes in Land Surface Models (LSM) for estimating water and energy balances still relies heavily on empirical equations that require calibration oriented to site-specific characteristics. When multiple parameters are used, different combinations of parameter values can produce equally acceptable results, leading to a risk of obtaining “right answers for wrong reasons”, compromising the reproducibility of the simulations and limiting the ecological interpretability of the results. To address this problem and reduce the need for free parameters, here we present novel formulations based on first-principles to calculate key components of water and energy balances, extending the already parsimonious SPLASH
model v.1.0 (Davis et al. 2017, GMD). We found analytical solutions for many processes, enabling us to increase spatial resolution and include the terrain effects directly in the calculations without unreasonably inflating computational demands. This calibration-free model estimates quantities such as net radiation, evapotranspiration, condensation, soil water content,
surface runoff, subsurface lateral flow and snow-water equivalent. These quantities are derived from readily meteorological data such as near-surface air temperature, precipitation and solar radiation, and soil physical properties. Whenever empirical formulations were required, e.g. pedotransfer functions and albedo-snow cover relationships, we selected and optimized the
best-performing equations through a combination of remote sensing and globally distributed terrestrial observational datasets. Simulations at global scales at different resolutions were run to evaluate spatial patterns, while simulations with point-based observations were run to evaluate seasonal patterns using data from hundreds of stations and comparisons with the VIC-3L model, demonstrating improved performance based on statistical tests and observational comparisons. In summary, our model offers a more robust, reproducible, and ecologically interpretable solution compared to more complex LSMs.
model v.1.0 (Davis et al. 2017, GMD). We found analytical solutions for many processes, enabling us to increase spatial resolution and include the terrain effects directly in the calculations without unreasonably inflating computational demands. This calibration-free model estimates quantities such as net radiation, evapotranspiration, condensation, soil water content,
surface runoff, subsurface lateral flow and snow-water equivalent. These quantities are derived from readily meteorological data such as near-surface air temperature, precipitation and solar radiation, and soil physical properties. Whenever empirical formulations were required, e.g. pedotransfer functions and albedo-snow cover relationships, we selected and optimized the
best-performing equations through a combination of remote sensing and globally distributed terrestrial observational datasets. Simulations at global scales at different resolutions were run to evaluate spatial patterns, while simulations with point-based observations were run to evaluate seasonal patterns using data from hundreds of stations and comparisons with the VIC-3L model, demonstrating improved performance based on statistical tests and observational comparisons. In summary, our model offers a more robust, reproducible, and ecologically interpretable solution compared to more complex LSMs.
Date Issued
2024-05-24
Date Acceptance
2024-03-28
Citation
Geoscientific Model Development, 2024, 17 (10), pp.4229-4309
ISSN
1991-959X
Publisher
Copernicus Publications
Start Page
4229
End Page
4309
Journal / Book Title
Geoscientific Model Development
Volume
17
Issue
10
Copyright Statement
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
License URL
Identifier
https://gmd.copernicus.org/articles/17/4229/2024/
Publication Status
Published
Date Publish Online
2024-05-24