# Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem
## 1 Introduction  
The exchanges of carbon dioxide and water vapor fluxes between the atmosphere and forest ecosystems are dominated by a variety of abiotic and biotic factors [1; 2]. At present, a large body of available direct measurements of these fluxes as well as their related energy and environmental variables in forest stands using the eddy covariance method are being compiled, archived and distributed [3]. In recent two decades, these measurements have been extensively utilized by the science community to explore the broad mechanisms controlling the dynamic variation of carbon and water fluxes from hourly to decadal time scales [4; 5]. In addition, numerous efforts have focused on better quantifying and exploiting the magnitude and behavior involved in the interactions of carbon and water cycles [6; 7]. Although remarkable advances have been obtained in our understanding of the nature and mechanisms leading to the ongoing evolution of carbon and water fluxes, their responses to different types of disturbances, such as land use changes, nutrient deposition, COelevation and fires [8; 9; 10; 11] remain uncertain. In this context, accurate estimation of carbon and water fluxes of forest ecosystems is of particular importance for addressing the problems originated from global environmental change, and providing helpful information about carbon and water budgets for analyzing and diagnosing past and future climate change. Process-based models that characterize complex biophysical and ecophysiological processes of land-atmosphere coupling at different temporal and spatial scales are commonly employed to quantify carbon and water budgets. According to several recent studies in relation to data-model inter-comparison of carbon and water fluxes, the evidence is convincing that appreciable errors do indeed exist between observed and estimated values by process-based models [12; 13; 14]. More recently, data-model assimilation techniques have been proposed in order to reduce their predictive errors via assimilating high resolution multi-source remote sensing observations (e.g., surface temperature and soil moisture) into process-based models with various optimization algorithms, such as ensemble Kalman filter, three dimensional and four dimensional variational assimilation methods. However, these ecosystem models depend on a large number of constants and input variables, which are comparatively difficult to be acquired due to high heterogeneity over land surface [15; 16]. It is therefore vital that much attention should be given to further enhance the estimates of carbon and water fluxes. Generally, when addressing the improvement of the predictability of different issues in ecological, hydrological and environmental fields, one of the most important challenges that needs to be considered is the specific non-linear problems in the dynamically varying systems [17; 18]. In the last decade, the use of data-driven techniques has become increasingly popular in abovementioned fields [19; 20; 21], due to their ability to deal with nonlinearity in time series [22; 23]. Among all data-driven techniques, artificial neural network (ANN) and support vector machine (SVM) have been broadly utilized [24; 25; 26; 27; 28; 29; 30], aiming at a better understanding the underlying mechanisms that dominate the carbon and water fluxes, and representing the complex processes involved in the carbon and water exchanges. These studies have concentrated primarily on exploiting the relative contributions of the driving forces for the evolution of carbon and water fluxes [24], interpolating the missing carbon and water data according to the flux tower measurements [25], and quantifying the nonlinear processes of carbon and water interactions mainly at site scale [26] and regional scale using the upscaling techniques [27; 28]. In addition, these data-driven techniques are increasingly used to correct the carbon and water flux errors estimated by the process-based models [29; 30].