Winter water allocation in regulated plain river networks requires an operational link between hydrological low water benchmarks and replenishment decisions. This study focused on the Lixiahe plain river networks; utilizing winter water level records from nine stations during 2006–2021, five hydrological methods were applied to derive candidate water level benchmarks. A one-dimensional MIKE 11 model was applied to simulate total upstream replenishment scenarios of 40–400 m³/s and to establish station-specific stage–discharge relationships under fixed downstream boundaries and structure operation rules. Within the simulated range, the candidate benchmarks corresponded to scenario-specific replenishment requirements of 153.94–268.49 m³/s. The framework supports comparison of winter water allocation strategies and identification of controlling stations, but ecological sufficiency requires independent evidence from species, habitat, thermal, dissolved oxygen, and water quality responses.

Maintaining water ecological health is essential for the sustainable development of plain water-network regions, where natural hydrological conditions and intensive human activities jointly shape ecological processes. To address the limited understanding of driving pathways, system-level contributions, and key internal factors affecting water ecological health, this study focused on the Lixiahe Plain, a typical plain water-network region in China. A water ecological health assessment system was developed based on the DPSIRM framework, and the Water Ecological Health Index (WEHI) was calculated at a 1 km grid scale from 2000 to 2020. PLS-SEM, RDA-VPA, and XGBoost-SHAP were further integrated to identify subsystem pathways, independent and interactive explanatory contributions, and key driving factors. The results showed that WEHI exhibited an overall fluctuating upward trend, increasing from 0.541 in 2000 to 0.594 in 2020, with the lowest value of 0.511 observed in 2010. Spatially, high-WEHI areas were mainly distributed in the southern and central–eastern regions and gradually expanded. PLS-SEM revealed stage-dependent differences in the direction and magnitude of subsystem effects, with the explained variance increasing from 21.7% in 2010 to 70.9% in 2020. The state and impact subsystems were the main carriers of WEHI variation. XGBoost-SHAP further identified vegetation coverage, RSEI, and soil moisture as key explanatory factors. The integrated results reveal the multilevel mechanisms underlying water ecological health in the Lixiahe Plain and provide a scientific basis for spatially differentiated ecological restoration and sustainable water and ecosystem management in plain water-network regions.

Hydroclimatic variability and intensive human regulation have reshaped surface water dynamics in plain river network regions (PRNs), altering both water extent and stability. Unraveling the frequency structure of surface water and its nonlinear associations is critical for sustainable water resource management. Based on Landsat-derived surface water dynamics from 2015 to 2025, this study constructed water occurrence frequency (WOF) indicators for Lixiahe Plain (LP) and employed XGBoost-SHAP to identify key natural and human factors, nonlinear responses, and interaction effects. The results showed that: (1) the annual water surface ratio (WSR) exhibited a phased inverted-U pattern, increasing from 5.22% in 2015 to 6.04% in 2018, remaining high during 2018–2021, and decreasing to 4.57% in 2025; (2) WOF revealed marked stability restructuring, with low-frequency water accounting for 31.0–53.7% of annual water and becoming more dominant after 2023; (3) the XGBoost-SHAP models showed reliable performance, with R² of 0.714–0.813 and RMSE below 0.018. Natural factors contributed more than human factors, and wind speed (WS), normalized difference vegetation index (NDVI) related features, and cropland ratio (CropR) were the influential factors, with approximate response transitions of 2.5–3.0 m s⁻¹, 0.50–0.52, and 65–68%. These findings support refined water resource management and sustainable development in PRNs.