Global warming has been intensifying the water cycle, thereby altering regional climate systems and hydrological processes. This is particularly the case for the Poyang Lake Basin (PLB) in monsoon-controlled southeast China, where climate changes and human activities are evident. Our study aims to quantify the contributions of climate change and human activities to the spatiotemporal variations of the relevant variables across meteorological and hydrological compartments on the basin scale. This study applies the moving t-test, Mann–Kendall test, and linear regression models to quantify the impacts of climate change and human activities on changes in streamflow and lake level from 1960 to 2019. Results show that precipitation, streamflow, and air temperature have increased, but Poyang Lake level has declined. Change points in streamflow trends are identified in 1991 and 2002 and in lake level in 2003. Contribution analysis indicates that climate change is the primary driver of increased streamflow. However, after 2002, the contribution of climate change declined, while that of human activities increased. The abrupt decline in lake level is mainly attributed to anthropogenic interventions. These findings identify the dominant factors of hydrological change and provide guidance for ensuring water security and sustainable water resource management in the basin.

Soil moisture (SM) is a pivotal state variable of the terrestrial hydrosphere, modulating energy partitioning, agricultural productivity and extreme-event propagation. This study analyzes 43 years (1982–2024) of data to assess soil moisture (SM) dynamics in the Yellow River Basin (YRB). Results indicate a statistically significant basin-wide SM decline across weekly, monthly, and annual scales, with grid-scale slopes ranging from −2.26 × 10⁻⁴ to 8.32 × 10⁻⁵ m³ m⁻³ month⁻¹. Spatially, non-farm areas retain higher SM than cultivated lands, with a distinct upstream-to-downstream variability pattern. While alpine headwaters show moistening, pervasive drying characterizes mid- and lower-catchments. Critically, transitional landscapes are approaching tipping points, risking shifts into persistently wetter or drier stable states where minor perturbations could lock ecosystems into new conditions. This underscores the urgent need for targeted climate-adaptation interventions. Generalized additive modeling identifies surface net solar radiation, soil temperature, and vapor pressure deficit as dominant drivers across multiple temporal scales. Their respective contributions, averaged across the basin, accounted for 29.4%, 25.3%, and 23.0% of the explained variance. Additionally, actual evapotranspiration emerged as a significant driver on the weekly scale, particularly within the center of the basin. These findings enhance process-based understanding of SM variability and provide a scientific foundation for adaptive water-resource management in the YRB.

Irrigation practice has impacts on the natural environment by changing the water and energy balance at the land surface and thereby interacting with the atmosphere. To quantify such impacts and estimate irrigation water demand, process-based hydrological models with a representation of irrigation practice are often used. However, the applicability of existing irrigation schemes is limited to arid and semi-arid regions. Likewise, it is still lack of more sophisticated irrigation schemes that can be particularly applicable to humid regions. This study presents the newly developed Crop-classified Dynamic Irrigation (CDI) scheme that has been two-way coupled into the land surface-hydrologic model Noah-HMS. Such development allows to distinguish the different irrigation practices for "rice" and "non-rice" crops and to estimate irrigation water demand. We have applied the newly developed model to an important grain and industrial crop production base in southern China, namely, Poyang Lake Basin (PLB), where the sown area of rice accounts for more than 60% of the sown area of all crops. As compared to the widely used, traditional Dynamic Irrigation (DI) scheme, the CDI-incorporated Noah-HMS improves the simulations of water and energy balance over the PLB from 2007 to 2015, especially irrigation water amount simulation. The relative error for irrigation water amount of CDI (DI) is -18.1% (-56.8%). In terms of surface water balance, the inclusion of irrigation practice has larger impacts on the simulated soil moisture (+1.7%) during dry years than that (+0.9%) during wet years, while has larger impacts on the simulated surface runoff (4.6%) in wet years than that (2.4%) in dry years. In terms of surface energy balance, irrigation practice leads to increased latent heat flux by 0.9 W/m2 (1.4%), decreased sensible heat flux by 0.5 W/m2 (1.3%), decreased ground heat flux by 0.02W/m2 (5.0%), and increased net radiation by 0.09 W/m2 (0.1%). Such impacts on the surface water and energy balance become more pronouncing at local scale especially over the intensively irrigated areas, for example the Nanchang city region. We conclude that our Crop-classified Dynamic Irrigation scheme is especially beneficial for applications in multiple cropping humid regions. Furthermore, our modeling development has the potential to be further extended into the fully coupled atmospheric-hydrologic modeling systems with a more holistic representation of human activities.