# A Typical Small Watershed in Southwestern China Is Demonstrated as a Significant Carbon Sink
###### Abstract  
Small watersheds are fundamental units for natural processes and social management in Southwestern China. Accurately assessing carbon sinks in small watersheds is crucial for formulating carbon sink management policies. However, there has been a lack of assessment of the dynamics of carbon fluxes in the major ecosystems of small watersheds. Here, we selected the Reshuihe River watershed, which is a typical small watershed in Southwestern China, to measure carbon fluxes using eddy covariance systems for two years (October 2021 to September 2023) from three major ecosystems, namely forest, cropland, and non-timber forest. We compared variations and controlling factors of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (Re) among different ecosystems, and estimated annual watershed carbon flux based on the land cover areas of the three ecosystems. This study found that three ecosystems were net annual carbon sinks during the study period. Forest was the strongest (\(-592.8\) and \(-488.1\) gC m\({}^{-2}\) a\({}^{-1}\)), followed by non-timber forest (\(-371.0\) gC m\({}^{-2}\) a\({}^{-1}\)), and cropland was the smallest (\(-92.5\) and \(-71.6\) gC m\({}^{-2}\) a\({}^{-1}\)), after taking falllow period into account. Weeds were a significant source of carbon flux in non-timber forest ecosystems. It was also found that variations in daily NEE were controlled by photosynthetically active radiation and soil volumetric water content, with weak effects related to temperature also being observed. However, when the temperature exceeded \(21\) degC, GPP and Re were significantly reduced in cropland. Finally, it was discovered that the total carbon sink of the three ecosystems in the watershed for one year was \(-52.15\) Gg C. Overall, we found that small watersheds dominated by forest ecosystems in Southwestern China have a strong carbon sink capacity. small watershed; carbon fluxes; forest-cropland-non-timber forest ecosystems; eddy covariance; path analysis 2024 13458. [[https://doi.org/10.3390/land13040458](https://doi.org/10.3390/land13040458)]([https://doi.org/10.3390/land13040458](https://doi.org/10.3390/land13040458))  
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method. This method utilizes an atmospheric transport model and CO\({}_{2}\) concentration, in conjunction with carbon emissions from human activities, to estimate the regional carbon sink. However, carbon flux data based on the atmospheric inversion method has low spatial resolution and cannot accurately differentiate carbon fluxes among different ecosystems [6], thus making it difficult to apply this method to assess the carbon budget of small watersheds. 'Bottom-up' approaches mainly include the application of process-based ecosystem models and the eddy covariance method [5]. Ecosystem process models simulate the carbon cycle with drivers to estimate carbon flux data across multiple timescales; however, there are uncertainties in the model parameters [6], which makes it more difficult for the models to accurately obtain carbon flux data in small watersheds. Eddy covariance is a method of directly observing the net carbon exchange between ecosystems and the atmosphere through in-situ monitoring [7]. These observations are then scaled up to determine the regional carbon balance [8; 9]. In recent years, studies have shown that 'top-down' estimates of carbon sinks in Southwestern China are highly uncertain, and that the size of the land sink in this region has been estimated differently [10; 11]. Therefore, it is imperative to conduct more accurate carbon flux observations using eddy covariance in Southwestern China. The characteristics of small watersheds determine the complexity of carbon flux studies. On the one hand, various ecosystems have their own unique structures and functions, and all simultaneously exchange carbon with the atmosphere as either carbon sources or sinks, leading to large uncertainties in the estimation of carbon flux components [4; 12].