Interpretable Machine Learning Reveals the Crucial Role of Water Availability in Regulating Thermal Optimality of Terrestrial Ecosystems

• Published in: Journal of geophysical research. Machine learning and computation Vol. 2; no. 2
• Main Authors: Ahmadi, Arman, Mallick, Kanishka, Yi, Koong, Baldocchi, Dennis
• Format: Journal Article
• Language: English
• Published: Wiley 01.06.2025
• ISSN: 2993-5210; 2993-5210
• DOI: 10.1029/2024JH000445

Gross primary productivity (GPP) is the total carbon dioxide plants fix in terrestrial ecosystems through photosynthesis. Air temperature directly influences photosynthesis and, consequently, ecosystem‐level GPP. However, air temperature is not the sole driver of GPP; ecosystem‐level GPP is a complex, multidimensional phenomenon where soil and atmospheric water availability play crucial roles. This study employs process‐based interpretable machine learning to investigate the thermal optimality of terrestrial ecosystems multidimensionally and evaluate the role of water availability in regulating thermal behavior and productivity. Our innovative data‐driven approach transcends traditional photosynthesis‐temperature response curves, visualizing the controlling effects of water availability in a three‐dimensional temperature‐moisture‐productivity space. We analyze 112,683 daily data samples of carbon, water, and energy fluxes alongside auxiliary micrometeorological variables from 108 eddy‐covariance sites across North America. Our multifaceted, observation‐driven approach quantifies the coupled influence of water availability and temperature on productivity. Findings highlight the critical role of long‐term ecosystem wetness and daily water availability in shaping terrestrial ecosystems' thermal behavior and optimality. Arid ecosystems tend to reach their optimum productivity at lower temperatures, with water availability as the primary productivity driver. Conversely, air temperature is the main productivity driver in wet ecosystems, accompanied by higher values for optimum temperature. Additionally, we observe an increasing air temperature trend in North America, which could cause a decline in productivity. However, thermal acclimation may counteract or mitigate this process. Plain Language Summary Terrestrial ecosystems' productivity is influenced by air temperature, but water availability also plays a crucial role. This study uses interpretable machine learning to examine how water availability regulates temperature influence on ecosystem productivity. Using over 112,000 daily data samples from 108 micrometeorological sites across North America, we visualize the interactions between temperature, water, and productivity in a three‐dimensional space. Our multifaceted approach quantifies how the coupled effects of water availability and temperature impact productivity. Our findings show that arid ecosystems reach their optimum productivity at lower temperatures, with water availability being the primary driver of productivity. In contrast, wet ecosystems are primarily influenced by air temperature and have higher temperature optima. Additionally, we observe an upward trend in air temperatures in North America, which could reduce productivity, although terrestrial ecosystems may acclimate to these higher temperatures. Our innovative data‐driven approach can be adapted to explore complex interactions in diverse ecosystems. Key Points Interpretable machine learning reveals temperature‐water interactions shaping productivity, transcending traditional photosynthesis models Water availability critically influences thermal optimality in terrestrial ecosystems, particularly in arid conditions Rising air temperatures in North America may reduce ecosystem productivity, while acclimation can mitigate this process