River-lake health assessment is critical for ecological governance, but overestimation risk induced by dual uncertainties (predictive uncertainty of indicator values and methodological uncertainty of weights) compromises the reliability of assessment results and governance decisions. To address this gap, this study proposes a general risk analysis framework integrating dual uncertainty quantification and overestimation risk coupling. First, a multi-dimensional assessment system with 12 key indicators (covering hydrology, water quality, aquatic biology, and social services) was established via Meta-analysis and quantitative screening. For uncertainty quantification: the Physics-Informed Neural Networks (PINN) model was used to characterize indicator value uncertainty based on 1264 historical monitoring samples; four complementary weighting methods (AHP, EWM, CRITIC, PCA) were integrated with a game theory-based framework (Nash equilibrium) to resolve weight conflicts, and weight uncertainty was quantified via normal distribution assumption (mean = coordinated weight, standard deviation = 1/10 of mean). The First-Order Second-Moment (FOSM) method was then adopted to establish a coupled “dual uncertainties-overestimation risk” model, quantifying the probability and magnitude of overestimation risk. Validated in Poyang Lake (China’s largest freshwater lake), results identified total phosphorus (TP), total nitrogen (TN), and Fish Biological Loss Index (FBLI) as high-risk indicators, with maximum allowable thresholds of 5.10–7.40, 7.04–9.38, and 6.05–9.02 across risk levels (1–50%), respectively. The comprehensive overestimation risk score ranged from 74.16 to 81.17, providing actionable thresholds for governance. This framework systematically addresses the insufficient consideration of dual uncertainties in existing studies, offering a scientific and operable tool for improving the reliability of river-lake health assessment and supporting targeted ecological protection decisions globally.

Seasonal hydrological fluctuations can substantially reshape river–lake networks, but changes in water extent, network morphology, and structural connectivity may not occur synchronously. This study investigated wet–dry seasonal variations in the Poyang Lake Basin using Landsat imagery from eight representative years between 1990 and 2025. Surface water was extracted using a consistent NDWI threshold, and 30 m DEM-derived channel information was used to correct interrupted, duplicated, and spurious river centerlines before Horton–Strahler ordering and graph-theoretic analysis. Mean water area increased from 3407.14 km² in the dry season to 6872.87 km² in the wet season, while mean river-network density increased from 0.0348 to 0.0461 km/km². The maximum Strahler order increased from III in the dry season to IV in the wet season. River-network complexity increased from a dry-season mean of 12.85 to 58.38 in the wet season, and the river-network development coefficient increased from 3.28 to 13.59, indicating a substantially more developed tributary and hierarchical structure under wet-season conditions. In contrast, the mean α, β, and γ indices increased by only 9.8%, 4.6%, and 4.3%, respectively, and dry-season values exceeded wet-season values in some years. From 1990 to 2025, wet-season water area increased by 11.11%, whereas river-network length decreased by 2.77%. These results demonstrate that seasonal changes in water extent, river-network morphology, and structural connectivity are asynchronous, highlighting the need to distinguish these complementary dimensions when evaluating large river-connected lake systems.