Articles | Volume 19, issue 18
https://doi.org/10.5194/gmd-19-8801-2026
https://doi.org/10.5194/gmd-19-8801-2026
Development and technical paper
 | 
21 Sep 2026
Development and technical paper |  | 21 Sep 2026

Parameter estimation for land-surface models using Neural Physics

Ruiyue Huang, Claire E. Heaney, and Maarten van Reeuwijk

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Cited articles

Arsenault, K. R., Nearing, G. S., Wang, S., Yatheendradas, S., and Peters-Lidard, C. D.: Parameter Sensitivity of the Noah-MP Land Surface Model with Dynamic Vegetation, J. Hydrometeorol., 19, 815–830, https://doi.org/10.1175/jhm-d-17-0205.1, 2018. a
Bezgin, D. A., Buhendwa, A. B., and Adams, N. A.: JAX-Fluids: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows, Comput. Phys. Commun., 282, 108527, https://doi.org/10.1016/j.cpc.2022.108527, 2023. a
Bradle, B.: A friendly introduction to numerical analysis, Pearson Education, ISBN 8131709426, 2007. a
Brutsaert, W.: Hydrology: An Introduction, Cambridge University Press, ISBN 978-0521824798, 2005. a
Carslaw, H. S. and Jaeger, J. C.: Conduction of Heat in Solids, Oxford University Press, USA, 81 pp., ISBN 0198533039, 1959. a
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This paper uses the Neural Physics approach to determine parameters of a simple land-surface model. We show that we can only obtain a reliable parameter estimation using soil temperature measurements at more than one depth, and that latent and sensible heat fluxes cannot be differentiated. We then apply the inverse model to real urban flux tower data and show that parameters, as well as various heat fluxes, can be reliably estimated using an observed value for the effective surface albedo.
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