Radiation Partitioning in a Cloud-Rich Tropical Mountain Rain Forest of the S-Ecuadorian Andes for Use in Plot-Based Land Surface Modelling
SSRN, ISSN: 1556-5068
2024
- 32Usage
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The understanding of how incoming shortwave radiation is divided into direct and diffuse components is crucial for comprehending ecosystem energy flows and carbon storage. Accurate partitioning functions are necessary for modeling studies, often carried out with land surface models (LSM) coupled with climate models. However, these functions largely rely on regional cloud and aerosol conditions. Data for developing semi-empirical partitioning functions are mostly available for the mid-latitudes, and there is a lack of knowledge about their performance in the tropics. This particularly holds for tropical forest ecosystems in the high Andes, where ground-based data on cloudiness and irradiation components are typically scarce. As a result, it is uncertain whether commonly applied functions can be utilized in regional LSMs without adaptation. This study evaluates a unique dataset of shortwave radiation components for a tropical mountain rainforest (MRF) in southern Ecuador. It also derives and tests a locally adapted shortwave radiation partitioning function based on machine learning, compares its performance with other semi-empirical functions, and evaluates the impact of partitioning functions on simulated outgoing shortwave radiation with an LSM. The findings reveal a consistently high cloud cover and prevailing diffuse radiation fluxes, especially during the rainy season. A higher proportion of direct irradiation is observed in the shorter, relatively dry season (Veranillo del Niño). The locally derived partitioning function performs well and outperforms other commonly used functions. Consequently, simulations of outgoing shortwave radiation using a plot-based LSM demonstrate improved performance when implementing the derived partitioning function. These results emphasize the high importance of locally and regionally adapted radiation partitioning functions based on machine learning and a more comprehensive feature set. The adapted LSM will be further utilized for heat flux and carbon sequestration studies.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know