Grigusova, P.; Limberger, O.; Murkute, C.; Pucha, F.; González-Jaramillo, V.; Fries, A.; Windhorst, D.; Breuer, L.; de Paula, M.D.; Hickler, T.; Trachte, K. & Bendix, J. (2025): <b>Radiation partitioning in a cloud-rich tropical mountain rain forest of the S-Ecuadorian Andes for use in plot-based land surface modelling</b>. <i>Dynamics of Atmospheres and Oceans</i> <b>110</b>, 101553.
Resource Description
Title:
Radiation partitioning in a cloud-rich tropical mountain rain forest of the S-Ecuadorian Andes for use in plot-based land surface modelling
FOR816dw ID:
575
Publication Date:
2025-05-05
License and Usage Rights:
Resource Owner(s):
Individual:
P. Grigusova
Contact:
email:
webmaster <at> lcrs.de
Individual:
O. Limberger
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email:
webmaster <at> lcrs.de
Individual:
C. Murkute
Contact:
email:
webmaster <at> lcrs.de
Individual:
F. Pucha
Contact:
email:
webmaster <at> lcrs.de
Individual:
V.H. González-Jaramillo
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email:
webmaster <at> lcrs.de
Individual:
A. Fries
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email:
webmaster <at> lcrs.de
Individual:
D. Windhorst
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email:
webmaster <at> lcrs.de
Individual:
L. Breuer
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email:
webmaster <at> lcrs.de
Individual:
M. Dantas de Paula
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email:
webmaster <at> lcrs.de
Individual:
T. Hickler
Contact:
email:
webmaster <at> lcrs.de
Individual:
K. Trachte
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email:
webmaster <at> lcrs.de
Individual:
J. Bendix
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email:
webmaster <at> lcrs.de
Abstract:
Understanding the partitioning of downward shortwave radiation into direct and diffuse components is essential for modeling ecosystem energy fluxes. Accurate partitioning functions are critical for land surface models (LSMs) coupled with climate models, yet these functions often depend on regional cloud and aerosol conditions. While data for developing semi-empirical partitioning functions are abundant in mid-latitudes, their performance in tropical regions, particularly in the high Andes, remains poorly understood due to scarce ground-based measurements. This study analyzed a unique dataset of shortwave radiation components from a tropical mountain rainforest (MRF) in southern Ecuador, developing and testing a locally adapted partitioning function using Random Forest Regression. The model achieved high accuracy in predicting the percentage of diffuse radiation (%Dif; R2=0.95, RMSE = 5.33, MAE = 3.74) and absolute diffuse radiation (R2=0.99, RMSE = 5.30, MAE = 14). When applied to simulate upward shortwave radiation, the model outperformed commonly used partitioning functions achieving the lowest RMSE (8.62) and MAE (5.82) while matching the highest R2 (0.97). These results underscore the importance of regionally adapted radiation partitioning functions for improving LSM performance, particularly in complex tropical environments. The adapted LSM will be further utilized for studies on heat fluxes and carbon sequestration.
