Mulched drip irrigation for maize cultivation has been widely implemented in the Xiliao River Plain in Northeast China in recent years. However, the effects of the change in irrigation method on soil water content and groundwater recharge in this area still remains uncertain. In this study, soil water content under mulched drip irrigation and flood irrigation was measured through field experiments. Soil water movement in the entire growing season under the two irrigation methods was simulated for the quantitative analysis of groundwater recharge by the Hydrus-2D model. Results showed that soil water content under mulched drip irrigation was generally larger than that of flood irrigation in the initial growth stage. However, an opposite trend was observed in the main growth stage. The simulated results indicated that the cumulative water fluxes of flood irrigation were greater than the values of mulched drip irrigation. Moreover, while infiltration depth under flood irrigation reached the maximum simulated depth (400 cm), infiltration depth under mulched drip irrigation was only 325 cm. The results of this study showed that mulched drip irrigation reduced the infiltration depth and groundwater recharge to some extent in the Xiliao River Plain. Such results are helpful in determining the influence of mulched drip irrigation on groundwater and can be a reference for the maintenance of the sustainability of regional groundwater in the large-scale promotion of mulched drip irrigation.

Irrigation performance assessment is an important step in ensuring sustainable agricultural development and improving the capacity of irrigation water management. In this paper, we examined irrigation water management in the Jiamakou Irrigation Scheme in the province of Shanxi (China) using four typical indicators, namely adequacy, efficiency, dependability, and equity. These indicators were calculated both globally and by grouping the data according to different irrigation processes (water allocation and water delivery). Results show that water was poorly allocated, and crop water requirements calculated through the FAO56 Penman-Monteith method was not met timely by scheduled water. They indicate that the overall performance of the scheme was relatively poor, owing to dismal water allocation. Nevertheless, water delivery performance was good, indicating that the scheme was able to deliver adequate and dependable water in an equitable and efficient manner. Our findings suggest that farmers should be trained in allocating irrigation water. The study provides insights into the current behavior of the irrigation scheme and may be used as a base for improvement of irrigation performance.

Due to the effects of global warming, the vapor pressure deficit (VPD) will inevitably and continuously rise in future, which is a fact that must be faced when discussing the sustainable development of vegetation ecosystems. Understanding the response of vegetation to changes in the atmospheric moisture state is necessary for minimizing the adverse effects of climate change. Using the leaf area index (LAI) and meteorological data in the Yellow River Basin collected over nearly 40 years, we explore the spatiotemporal variations in VPD and vegetation within the growing season and the differences in the effects of VPD on vegetation in different climatic zones. The results show the following: (1) both VPD and LAI in the Yellow River Basin show an increasing trend, in which VPD increases radially outward centered on the arid regions, while the vegetation develops favorably over almost the whole basin; (2) at the whole-basin scale, the area of VPD positively correlated with vegetation growth is greater than the area of negative correlation, and this overall trend is increasing significantly; (3) the VPD exerts heterogeneous effects on different vegetation in different climatic zones, and these heterogeneity findings suggest that the effects of VPD on vegetation are more influenced by differences among the climatic zones of the vegetation than by differences in the vegetation types themselves. Our results provide insights into the effects of atmospheric moisture on vegetation growth and provide references for the sustainable development of vegetation ecosystems and future safeguarding strategies of vegetation over large spatial scales.

Drought forecasting for basin water management requires accounting for precipitation deficits, climatic water balance, and soil-water storage across appropriate timescales. This study evaluates index timescales, lead times, and five machine learning models for the Yellow River Basin using 21 ERA5-Land variables from 1990 to 2025. We constructed the standardized precipitation index (SPI), Penman–Monteith standardized precipitation evapotranspiration index (SPEI-PM), and root-zone standardized soil moisture index (SSI-rootzone). A staged comparison used training-window standardization and rolling-origin cross-validation, followed by evaluation during 2021–2025. Scale screening retained 90-day indices. At a seven-day lead, histogram gradient boosting achieved R² values of 0.807, 0.852, and 0.995 for SPI, SPEI-PM, and SSI-rootzone, respectively. R² remained positive at 30 days but became negative at 90 days. Across 8548 grid cells, forecasts reproduced drought-area dynamics but underestimated severe and extreme drought extent, particularly for SPEI-PM. GPM and SMAP, excluded from model development, provided external comparisons; independently derived indices correlated with SPI and SSI-rootzone forecasts at 0.669 and 0.517. These findings show that evaluating forecasts for drought preparedness requires consideration of temporal aggregation, lead time, spatial extremes, and product dependence rather than basin-average accuracy alone.