Model Selection for Ecosystem Respiration Needs to Be Site Specific: Lessons from Grasslands on the Mongolian Plateau

Abstract

Selecting an appropriate model for simulating ecosystem respiration is critical in modeling the carbon cycle of terrestrial ecosystems due to their magnitude and high variations in time and space. There is no consensus on the ideal model for estimating ecosystem respiration in different ecosystems. We evaluated the performances of six respiration models, including Arrhenius, logistic, Gamma, Martin, Concilio, and time series model, against measured ecosystem respiration during 2014–2018 in four grassland ecosystems on the Mongolian Plateau: shrubland, dry steppe, temperate steppe, and meadow ecosystems. Ecosystem respiration increased exponentially with soil temperature within an apparent threshold of ~19.62 °C at shrubland, ~16.05 °C at dry steppe, ~16.92 °C at temperate steppe, and ~15.03 °C at meadow. The six models explained approximately 50–80% of the variabilities of ecosystem respiration during the study period. Both soil temperature and soil moisture played considerable roles in simulating ecosystem respiration with R square, ranging from 0.5 to 0.8. The Martin model performed better than the other models, with a relatively high R square, i.e., R2 = 0.68 at shrubland, R2 = 0.57 at dry steppe, R2 = 0.74 at temperate steppe, and R2 = 0.81 at meadow. These models achieved good performance for around 50–80% of the simulations. No single model performs best for all four grassland types, while each model appears suitable for at least one type of ecosystem. Models that oil moisture include models, especially the Martin model, are more suitable for the accurate prediction of ecosystem respiration than Ts-only models for the four grassland ecosystems.

Unlocking the power of machine learning for Earth system modeling: A game‐changing breakthrough

“…We can anticipate an increase in the development of ESMs that can be parameterized and run by non‐modelers and applied to broader applications, including ecosystem, landscape, and regional applications where spatial resolutions of 10–25 km and temporal resolutions of less than a month (e.g., day, hours, etc.) are often needed for location‐specific applications (e.g., Zou et al, 2022). Were ML tools applied with a small number of input variables, without the need for in‐depth knowledge of detailed processes and algorithms, and with accurate predictions as demonstrated in Sun et al (2023) and others (e.g., Pal & Sharma, 2021), our knowledge, resource management, adaptation strategies, and policy making would be significantly and promptly improved.…”