Desert terminal lakes are important signals to discern ecological degradation crises, particularly in arid areas where an artificial project of ecological water diversion has designated a quota of river water to prevent lake body shrinkage and protect the ecosystem. Knowledge of the minimum ecological water demand (EWD) is thus necessary to ensure the basic health of lake ecosystems. This study analyzed the spatiotemporal evolution of water boundaries using Landsat satellites data via remote sensing technology from 2002 to 2017 in East Juyan Lake, an inland desert terminal lake of the Heihe River in northwest China. The minimum lake water demand was determined using two estimation methods: the lake-evaporation-oriented EWD method and the minimum water level method. In the latter method, both lake topography (using water-level area curves) and biological survival demands (using bighead carps as indicators) were considered to derive the minimum lake EWD. Water diversion to the lake over the past 15 years has increased the lake’s area, but there are still marked intra-annual seasonal variations. The annual minimum lake water demand was suggested to be 54 × 10⁶ m³/year by comparing the different methods; however, it was not satisfied, and the lake survival was endangered when the occurrence frequency of the annual runoff in the Zhengyixia hydrological station exceeded 65%. This study offered promising directions for inland lake water resource management.

As an integrated framework linking resource use and environmental sustainability, the WEF (Water–Energy–Food) system plays a vital role in achieving sustainable agricultural development. Focusing on the irrigation districts in the lower reaches of the Yellow River, this study constructed and applied a Super-Undesirable-SBM (super-efficiency undesirable slacks-based measure) model and a GTWR (geographically and temporally weighted regression) model from a WEF perspective to systematically analyze the spatiotemporal evolution and driving mechanisms of WEFSE (Water–Energy–Food Synergistic Efficiency) from 2000 to 2020. The overall WEFSE exhibited a continuous upward trend, with the spatial pattern gradually shifting from the southwest to the northeast and regional disparities becoming more pronounced. The efficiency demonstrated a significant positive spatial autocorrelation, indicating a stable clustering pattern of “high–high” and “low–low” efficiency areas. In terms of driving mechanisms, WEFSE evolved from being dominated by socio-economic drivers to a composite system jointly influenced by ecological and structural factors. Among these, PD (population density) and WP (proportion of water area) had increasingly positive effects, whereas PRE (precipitation) and NDVI (normalized difference vegetation index) imposed notable constraints. Meanwhile, PCL (proportion of cultivated land), GP (proportion of grassland), and AT (average temperature) exhibited significant spatial differentiation. This study highlights that the assessment of WEFSE and identification of its driving mechanisms using the Super-Undesirable-SBM and GTWR models can help to uncover the spatiotemporal dynamics of agricultural resource utilization, providing methodological support and decision-making insights for optimizing resource allocation and promoting sustainable development in the Yellow River irrigation districts and other complex agricultural systems.

Irrigation districts are key operational units where water diversion, agricultural energy inputs, cultivated land use, and food production are directly coupled. However, existing water–energy–food nexus studies have mainly focused on provincial or basin scales, with limited attention to nonlinear associations and regional heterogeneity at the irrigation-district scale. This study examined the spatiotemporal evolution and associated factors of the agricultural water–energy–food (AWEF) coupling coordination degree (CCD) in 40 lower Yellow River irrigation districts from 2000 to 2020. An integrated framework combining the CCD model, XGBoost–SHAP, nonlinear fitting with bootstrap threshold analysis, and GTWR was used to identify leading associated factors, nonlinear transition points, and spatiotemporal heterogeneity. The AWEF CCD showed an overall upward trend from 2000 to 2020, with the mean value increasing from approximately 0.54 in 2000 to 0.77 in 2020. Spatially, the distribution gradually shifted from the lower-value classes of 0.48–0.55 and 0.55–0.62 in the early stage to the higher-value classes of 0.69–0.76 and 0.76–0.84 in the later stage. Population density (PD), nighttime light index (NLI), proportion of cultivated land (PCL) and proportion of water area (WP) were identified as the key associated factors. Their associations with CCD were not uniform across regions. Their relationships with CCD were nonlinear and region-specific: the SHAP contribution of PD changed from positive to negative across thresholds of 432.7–470.8 persons/km², whereas NLI changed from negative to positive at 7.655–8.472; PCL shifted from positive to negative at 0.8157–0.8241, while WP changed from negative to positive at approximately 0.006–0.012. GTWR further revealed spatiotemporal heterogeneity in the local relationships between these factors and CCD. The local coefficients of PD remained negative throughout the study period, whereas those of NLI remained positive but were generally lower during 2010–2020 than during 2000–2009. The local relationships of PCL and WP with CCD varied across regions and stages, with both positive and negative coefficients observed.

Understanding the interactions between water resource utilization, socioeconomic development, and the ecological environment is fundamental to ensuring ecological security and promoting sustainable development in the lower Yellow River region. This study developed a composite system integrating water resources, socioeconomic development, and ecological environment for 27 cities in the lower Yellow River Basin. The indicator weights were determined using the combined Analytic Hierarchy Process (AHP) and entropy weight method. Subsequently, several models, including Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the coupling coordination degree (CCD) model, spatial autocorrelation, and obstacle degree analysis, were integrated to systematically evaluate the spatiotemporal evolution, coordination patterns, and key constraints of the composite system. The findings indicate that the development of the three subsystems surged with fluctuations, following the gradient pattern of ecological environment > socio-economic development > water resources. The water resource subsystem improved slowly and remained the main constraint. The CCD gradually shifted from barely and primarily coordinated stages to intermediate and well-coordinated stages. Spatially, Shandong outperformed Henan, eastern areas exceeded western areas, and core cities showed higher coordination levels than inland cities. Significant and increasing positive spatial autocorrelation indicated strengthened spatial agglomeration, with high–high clusters in eastern Shandong and low–low clusters in western and northwestern Henan. The per capita water resource indicator emerged as the dominant obstacle, while forest and grassland area, the Normalized Difference Vegetation Index (NDVI), agricultural water use, and the share of the tertiary industry also constituted major constraints.