Hierarchical Rainfall-Intensity-Aware Hourly Precipitation Merging Based on Tree-Model Routing and LSTM Conditional Regression: Spatiotemporal Generalization and Hydrological Utility

Abstract

In mountainous regions with uneven gauge coverage, satellite precipitation errors are amplified by nonlinear rainfall–runoff processes, while conventional hourly merging often relies on a single continuous regression that inadequately handles zero inflation and intensity heterogeneity. We propose a hierarchical intensity-aware framework comprising a wet/dry gate, a frequency-matched four-class intensity router, and a shared long short-term memory (LSTM) network with class-conditional outputs. In the upper Fujiang River basin, GPM, CMORPH, ERA5-Land and topographic variables were used as predictors; models were trained on 2010–2013, with 2014 retained for temporally held-out validation across point and areal scales, spatial cross-validation and streamflow simulation. Progressive ablation shows that intensity stratification drives the main point-scale gain (Kling–Gupta efficiency (KGE), 0.22 → 0.43), while temporal modeling improves held-out catchment-event performance (KGE 0.75). Oracle diagnosis identifies intensity routing as the main remaining bottleneck, and attribution and source-ablation analyses show that data-source value varies with prediction stage and evaluation scale. Hydrologically, merged precipitation raises the overall Nash–Sutcliffe efficiency (NSE) from −0.13 (GPM) and −0.21 (CMORPH) to 0.40 and reduces absolute peak bias from 52–55% to 32%. Routing discrimination remains the principal residual limitation under the present architecture.