Abstract Blue water (BW) and green water (GW) resources are fundamental to the hydrological cycle, yet their effective future projection and mechanistic understanding remain challenging. Both physics‐based and data‐driven models have limitations in complex hydrological long‐term projection and nonlinear interpretation. Therefore, an Interpretable Physics‐Guided Deep Learning (IPGDL) framework was proposed, which not only preserves the hydrological physical mechanisms of the Soil and Water Assessment Tool (SWAT) but also leverages the efficient nonlinear learning capabilities of CVOA‐CNN‐BiLSTM (CCB), while incorporating the Shapley Additive Explanations (SHAP) method to enhance result interpretability. Quality‐assured CMIP6 climate and PLUS land use scenarios drive the physics‐guided CCB model to project blue‐green water (2022–2100), while SHAP interprets nonlinear mechanisms through global, interaction, time step, and spatial effects from historical and future scenario perspectives. The IPGDL framework was applied to the Xiangjiang River Basin (XRB), and key findings are as follows: (a) The framework achieves robust projections by coupling physical mechanisms with nonlinear processes, reducing potential biases in traditional approaches. (b) Nonlinear interpretation results reveal meteorological features dominate blue‐green water (46% and 53% importance), with critical threshold effects (e.g., precipitation thresholds of 140.5 and 109.4 mm for BW and GW), significant temporal lags and spatial effects governing nonlinear system behavior in the historical period (1991–2020). Future scenario interpretation results reveal that low‐forcing scenarios due to precipitation‐dominated mechanisms trigger more extreme responses (−43.6% BW, +34.8% GW) with smaller uncertainty compared to medium and high‐forcing scenarios. This IPGDL framework demonstrates potential for hydrological modeling and offers insights into watershed management.
Guo et al. (Fri,) studied this question.