分布式机器学习模型中上游径流联合分布对下游河流量预测不确定性的影响
Joint distribution of upstream runoff governs downstream river-discharge prediction uncertainty in distributed ML models
AI总结：
研究分布式水文模型中从集总到分布式概率预测转变的新要求，以日本为例，通过训练概率径流LSTM及路由方案，发现需关注径流不确定性空间联合结构，简单分位数匹配可恢复分散性。
摘要：
水文预测的不确定性量化对运营决策至关重要。近期生成式机器学习方法推动了概率径流预测，但局限于集总模型。确定性LSTM径流模型在网格或集水区尺度应用并通过河网生成连续排放场。本文指出从集总到分布式概率预测需联合采样上游径流生成的联合分布，以日本为例进行验证。
英文摘要：
Uncertainty quantification of hydrological predictions is necessary to inform operational decisions. Recent generative machine-learning methods have advanced probabilistic streamflow prediction, but have remained confined to lumped models that predict a basin outlet directly. At the same time, deterministic LSTM runoff models are increasingly applied at grid or catchment scale and routed through river networks to produce spatially continuous, physically consistent discharge fields. This technical note argues that moving probabilistic prediction from lumped to distributed models introduces a specific new requirement: the joint distribution of upstream runoff generation must be sampled jointly. In lumped inference, the model predicts the outlet distribution directly and can modulate spread from basin attributes. In distributed inference, downstream discharge is obtained by routing many upstream runoff predictions, so independent local sampling averages uncertainty away. Using Japan as a case study, we train two probabilistic basin-scale runoff LSTMs and route their runoff through a Hayami routing scheme. Randomly matching upstream ensemble members produces severely under-dispersed downstream ensembles, whereas a simple quantile matching strategy restores much of the spread of the direct basin-scale reference. The shift from lumped to distributed probabilistic hydrology therefore requires explicit attention to the spatial joint structure of runoff uncertainty.
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