Attentional BiLSTM with Ecological Process Constraints for Carbon–Water Flux Prediction in Cold, Temperate Coniferous Forests

Addressing the challenges in predicting carbon–water fluxes in cold, temperate coniferous forests—specifically, the strong heterogeneity of driving factors, the significant non-linearity of processes, and the lack of consistency of ecological mechanisms in data-driven models—this paper constructs a Multi-Channel Fusion Attention BiLSTM (MCF-ABiLSTM) model. This model is designed for the joint prediction of Net Ecosystem Exchange (NEE) and Latent Heat Flux (LE). The model adopts a multi-channel structure to separately characterize meteorological, soil, and historical flux information, combining channel attention and temporal attention mechanisms to enhance the identification of key driving factors and critical temporal scales. On this basis, dynamic Water Use Efficiency (dWUE) and Sensitivity of Carbon–Water (SCW) indices are proposed to characterize the synergistic response features of carbon uptake and evapotranspiration under humidity and temperature gradients. The stable ecological relationships revealed by these indices are explicitly introduced into the model training process as ecological process consistency constraints, thereby guiding the model to adhere to known physiological mechanisms while improving prediction accuracy. Experimental results demonstrate that the MCF-ABiLSTM model outperforms various benchmark models in predicting both NEE and LE. Furthermore, flux contribution decomposition results indicate that the model’s response structure to environmental drivers is highly consistent with the known carbon–water coupling mechanisms of cold, temperate coniferous forests. This study achieves organic integration of high-precision carbon–water flux prediction, ecological process constraints, and mechanism analysis, providing a modeling framework that possesses both predictive capability and ecological interpretability for research on the carbon–water cycle in cold, temperate forest ecosystems.