AI 中文总结
该研究提出雪水质量守恒感知机框架，在513个CAMELS-US流域验证其性能，发现两状态MCP网络平衡精度与复杂度，参数少于LSTM，为水文表征状态选择提供经验依据。
AI 中文摘要
质量守恒感知机（MCP）建立了一种建模范式，可将概念性水文模型重构为受物理约束、概念可解释的神经网络。本文开发了雪水MCP网络框架，并在513个CAMELS-US流域上进行评估。我们首先将耦合的两状态SOIL-MCP和SNOWMCP概念模型重构为质量守恒神经网络，结果表明该水文模型与神经网络公式的预测性能相当。随后，我们研究了两状态HYDROMCP架构内的跨节点状态信息共享，并评估了由1至5个状态的三种可解释MCP单元构建的更广泛单层网络。在整个美国大陆（CONUS），中位数KGEss从单状态网络的0.82提升至两状态网络的0.89，五状态网络则达0.90，表明超过两个状态后总收益递减。流域特定的MCP与LSTM选择得到的中位数KGEss均为0.90，而所选MCP网络平均使用更少参数。基于互补的AIC和KGE选择可识别出紧凑的流域特定有向图表征，平衡了预测精度与模型复杂度。这些分析为确定水文表征所需的状态数量、类型及相互作用提供了经验依据。未来研究应测试针对多种水文响应的联合训练，如径流、雪水当量和地下水储量。
英文摘要
The Mass-Conserving Perceptron (MCP) establishes a modeling paradigm in which conceptual hydrologic models can be reformulated as physically constrained, conceptually interpretable neural networks. Here, we develop a snow-water MCP network framework and evaluate it across 513 CAMELS-US basins. We first recast a coupled two-state SOIL-MCP and SNOWMCP conceptual model as a mass-conserving neural network and show that the hydrologic-model and neural-network formulations achieve comparable predictive performance. We then examine cross-node state-information sharing within two-state HYDROMCP architectures and evaluate broader single-layer networks constructed from three types of interpretable MCP units with one to five states. Across CONUS, the median KGEss increases from 0.82 for one-state networks to 0.89 for two-state networks and 0.90 for five-state networks, suggesting diminishing aggregate gains beyond two states. Basin-specific MCP and LSTM selection yields the same median KGEss of 0.90, while the selected MCP networks use fewer parameters on average. Complementary AIC- and KGE-based selection identifies compact, basin-specific directed-graph representations that balance predictive accuracy and model complexity. These analyses provide an empirical basis for identifying the numbers, types, and interactions of states needed for hydrologic representation. Future studies should test joint training against multiple hydrologic responses, such as streamflow, snow water equivalent, and groundwater storage.
Comments125 pages; Main text: 7 tables, 18 figures; Supplementary Materials: 3 texts, 10 tables, 32 figures
