Yue Xin

Plain river networks are weakly flushed and frequently regulated, which can decouple instantaneous water quality from biological responses. We integrated physicochemical variables, phytoplankton, zooplankton, benthic macroinvertebrates and fish environmental DNA (eDNA) to diagnose ecological condition in the Lixiahe plain river network, China. Regional water quality was assessed at 62 sites, and coupled analyses used 36 sites with complete multi-trophic data. Moderate pollution accounted for 62.90% of classified sites, with total nitrogen as the principal pressure. After Benjamini–Hochberg correction, seven independent associations with primary biological indicators remained significant, mainly linking TOC and TDS with planktonic metrics; the TOC-MEII correlation was treated separately as a non-independent composite association. Mean fish eDNA observed OTU richness declined to 22.33 at the three severely polluted matched sites, although grade-wise contrasts were descriptive because both extreme groups contained only three sites. Adjusted R² values for the four RDA models ranged from 9.8% to 11.4%; all overall fixed-seed permutation tests were significant (p = 0.0002–0.0298), indicating a detectable but limited water-quality signal. The study-specific MEII ranged from 29.50 to 67.42 (mean 48.04). These results show asynchronous trophic responses and support MEII as a preliminary within-survey screening tool that should be interpreted with its component sub-indices rather than as a calibrated reference-condition index.

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.

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.

The Yellow River Basin is an important grain-production base in China, playing a crucial role in the country’s agricultural production and overall national economy and social development. However, due to the impact of climate change, China’s food security is facing challenges. Therefore, this article takes the Yellow River Basin as an example to reveal the temporal and spatial evolution patterns of the main crop yields in the basin. Based on a coupled statistical downscaling model (SDSM) and ten General Circulation Models (GCMs) from CMIP5, it estimates the future temporal and spatial evolution characteristics of rainfall and evaporation in the basin. Furthermore, a distributed crop-growth model (AquaCrop) is constructed to reveal the temporal and spatial evolution patterns of agricultural irrigation water requirements from a future perspective, clarifying the impact of multi-source uncertainty on the prediction uncertainty of agricultural irrigation water needs. The results indicate that the ten climate models constructed in this study can be effectively applied to the Yellow River Basin, and their ability to capture light-rain events is superior to that of moderate- and heavy-rain events. The simulation accuracy of the AquaCrop model significantly improves with an increase in precipitation frequency. The agricultural irrigation water demand in the middle and upper reaches of the basin is greater than that in the lower reaches, and the uncertainties from GCMs and RCPs have a significant impact on the uncertainty of agricultural irrigation water demand. The research results provide important references for formulating agricultural development plans for irrigation areas under climate-change conditions and for developing response measures for irrigation areas to cope with climate change.