Discrepancies between water demand and supply are intensifying and creating a need for sustainable water resource process management associated with rapid economic development, population growth, and urban expansion. In this study, a scenario-based interval fuzzy-credibility constrained programming (SIFCP) method is developed for planning a water resource management system (WRMS) that can handle uncertain information by using interval values, fuzzy sets, and scenario analysis. The SIFCP-WRMS model is then applied to plan the middle route of the South-to-North Water Diversion Project (SNWDP) in Henan Province, China. Solutions of different water distribution proportion scenarios and varied credibility levels are considered. Results reveal that different water-distribution proportion scenarios and uncertainties used in the SIFCP-WRMS model can lead to changed water allocations, sewage discharges, chemical oxygen demand (COD) emissions, and system benefits. Results also indicate that the variation of scenarios (i.e., from S2 to S3) can result in a change of 9% over the planning horizon for water allocation in the industrial sector. Findings can help decision-makers resolve conflicts among economic objective, water resource demand, and sewage discharge, as well as COD emissions.

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.

With the rapid development of the social economy, human activities have increasingly disrupted water environments, and the continuous input of pollutants poses significant challenges for water environment management. Taking the Xiaoxingkai Lake basin as the study area, this paper develops a social–economic–water environment model based on the system dynamics methodology, incorporating subsystems for population, agriculture, and water pollution. The model focuses on four key indicators of pollution severity, namely, total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (NH₃-N), and simulates the changes in pollutant loads entering the river under five different scenarios from 2020 to 2030. The results show that agricultural non-point sources are the primary contributors to TN (79.5%) and TP (73.7%), while COD primarily originates from domestic sources (64.2%). NH₃-N is mainly influenced by urban domestic activities (44.7%) and agricultural cultivation (41.2%). Under the status quo development scenario, pollutant loads continue to rise, with more pronounced increases under the economic development scenario, thus posing significant sustainability risks. The pollution control enhancement scenario is most effective in controlling pollutants, but it does not promote socio-economic development and has high implementation costs, failing to achieve coordinated socio-economic and environmental development in the region. The dual-reinforcement scenario and moderate-reinforcement scenario achieve a balance between pollution control and economic development, with the moderate-reinforcement scenario being more suitable for long-term regional development. The findings can provide a scientific basis for water resource management and planning in the Xiaoxingkai Lake basin.

Evaluating and enhancing system resilience is essential to strengthen the regional ability to external shocks and promote the synergistic development of environment, economy and society. Taking the Upper Yellow River (UYR) as an example, this paper constructed a resilience evaluation index system for the environment—economy—society (EES) composite system. A three-dimensional space vector model was built to calculate the resilience development index (RDI) of three subsystems and the composite system from 2009 to 2022. Pathways supporting high resilience levels of the composite system were examined using the fuzzy-set qualitative comparative analysis (fsQCA) method from a configuration perspective. The results revealed that (1) the RDI of three subsystems and the composite system in the UYR showed an increasing trend; relatively, the environment and economy subsystems were lower, and their RDI fluctuated between 0.01 and 0.06 for most cities. (2) The emergence of high resilience is not absolutely dominated by a single factor, but rather the interaction of multiple factors. To achieve high resilience levels, all the cities must prioritize both environmental protection and economic structure as core strategic pillars. The difference is that eastern cities need to further consider social development and life quality, while western cities need to consider social development, life quality, and social security. Other cities including Lanzhou, Baiyin, Tianshui, and Ordos should focus on social construction and social security. Exploring the interactive relationship between various influencing factors of the resilience of the composite system from a configuration perspective has to some extent promoted the transformation from a single contingency perspective to a holistic and multi-dimensional perspective. These findings provide policy recommendations for achieving sustainable development in the UYR and other ecologically fragile areas around the world.

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.

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.