Investigation of eco-hydrogical processes influencing Himalayan Chir-Pine net ecosystem exchange using machine learning classifiers

Terrestrial ecosystems gain carbon through photosynthesis and lose it through respiration in autotrophs and heterotrophs. Continuously measured values of carbon fluxes of a forest ecosystem, particularly net ecosystem exchange (NEE) could be used as a general indicator of forest ecosystem functioning. Subsequently, quantification of the ecosystem functioning as a response to changes in the microclimate and environmental variables is necessary to frame sustainable adaptive measures and conservation policies. The Himalayan Chir- Pine (Pinus roxburghii Sarg.) is a gregarious, fire-resistant, indigenous tree species, often form pure forests and having the characteristics of high regeneration potential.  The Chir-Pine is widely distributed across the western and central part of the Indian Himalayan Region and thereby acts as a major control of land-atmosphere processes. In the recent years, studies have provided insights on sub-daily to annual scale interactions of Chir-Pine ecosystem with microclimatic and environmental variables, and it was reported that Chir-Pine ecosystem is a heat dominating ecosystem with high carbon sequestration potential. However, almost no information is available on environmental drivers resulting carbon sequestration of Himalayan Chir-Pine ecosystem. In this context, it is widely reported that the data driven models are well suited for identifying and prioritizing drivers for ecosystem carbon exchange. Therefore, this study is aimed at developing a data-driven model for predicting day-time net ecosystem exchange of a Chir-Pine forest of central Himalaya, Uttarakhand, India. And further aims to quantify driver-response relationship between net ecosystem exchange (NEE) and micro-climatic variables using machine learning classifiers. In order to address the objectives, high frequency (30-min) day-time observations of NEE and micrometeorological parameters during March, 2020 to December, 2022 are collected and compiled from a 30 m eddy covariance tower situated at Kosi-Katarmal, Almora, Uttarakhand, India (29º38'22"N, 79º37'2"E). Subsequently, four machine learning algorithms such as K-nearest neighbor, Naïve Bayes, support vector machine and decision trees are used to predict the day-time NEE using individual and combinations of predictors such as rainfall, net radiation, air temperature, soil moisture and soil temperature. To obtain a robust model, 100 times bootstrapping has been performed in each simulation where 2/3^rd of the dataset is used for training the model and rest is used for testing.  The model performance during training and testing has been assessed using receiver operator characteristics and the prioritization of the driver impacting NEE is carried out by identifying highest area under curve (AUC) value during model testing. The initial results indicate that the decision tree classifier is the best model amongst the four selected model for predicting day-time NEE of Chir-Pine ecosystem, and the best predictors having high AUCs are air-temperature, net-radiation and soil moisture. The prediction of the NEE through data-driven models of Chir-Pine ecosystem is expected to be beneficial for quantifying the regional scale extent of change in carbon fluxes under warmer scenarios.