The Three Gorges Reservoir (TGR) in China is one of the world’s largest hydropower projects. Interval inflow, originating from ungauged areas between the upstream gauging control stations (Zhutuo, Beibei, Wulong) and the TGR dam site, is a critical component of total reservoir inflow, but its hydrological characteristics have not been fully clarified. The accurate estimation and prediction of interval inflow are essential for reservoir safety and flood control operations. Using daily hydrological data from 2009 to 2017, we propose an integrated analytical framework combining (i) flow travel time estimation using cross-correlation analysis, (ii) multi-scale statistical characterization, and (iii) K-means clustering with bootstrap validation and algorithm comparison. This framework systematically identified hydrological regimes of interval inflow and their associated flood control risks. The key findings are as follows. (1) The optimal flow travel time from the upstream gauging stations to the dam site is 1 day (correlation coefficient ρ=0.9809,p<0.001), and it remains stable across different flow regimes. (2) The interval inflow exhibited a highly right-skewed distribution (mean 1279 m3/s, standard deviation 1651 m3/s) and contributed on average 10.1% to the total inflow. The contribution ratio exhibited an inverted U-shaped relationship with increasing total inflow, peaking at 11.4% when the total inflow (Q) was 13,014 m3/s. The quartile thresholds were 5788 m3/s, 9575 m3/s, and 16,869 m3/s (corresponding to Q1, Q2, and Q3, respectively), and the 10th and 90th percentiles (P10 and P90) were 4865 m3/s and 24,625 m3/s, respectively. (3) Five distinct hydrological patterns (C1–C5) were successfully identified, among which Cluster C4 (5.7% of days) was defined as the high-impact pattern based on reservoir operational criteria, with a mean I of 6425 m3/s, a mean R of 27.8% (up to 44% in extreme events), a mean flood duration of 5.8 days, a mean flood volume of 36.1 × 108 m3, and a flashiness index of 1.48. (4) C4 is predominantly triggered by localized heavy rainfall, and its flashy nature implies a substantially shorter forecast lead time compared with mainstream-dominated floods, posing major challenges to real-time reservoir operations. This study demonstrates that interval inflow risk is pattern-dependent and that the proposed framework provides a scientific basis for developing pattern-specific reservoir operation strategies. The proposed framework is transferable to other large river-type reservoirs facing similar ungauged interval inflow challenges.
Zhang et al. (Mon,) studied this question.