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Soil salinization is a manifestation of land degradation caused by the combined effect of natural processes and human activities. This issue is particularly common in irrigation districts, where various factors exacerbated by frequent human interventions influence soil salinity. To prevent soil salinization, it is essential to have a comprehensive understanding of soil water-salt transport mechanisms in irrigation districts with frequent human activities. After reviewing the factors that influence soil water-salt transport in irrigation districts and the characteristics and applications of water-salt transport model, we propose a potential direction for simulating water-salt transport in irrigation areas. The formation of salinization in irrigation areas is closely related to natural factors such as climatic aridity, terrain, seasonal soil freeze-thaw cycles, groundwater salinity, and parent material of soils, as well as anthropogenic factors such as irrational irrigation practices, agricultural methodologies, and fertilization regimes. To understand the process of water- salt transport, soil water- salt transport models are effective tools. The commonly used models for water-salt transport include water-salt balance models, physical models, and statistical models. They are mainly used to optimize irrigation water-saving regimes and drainage management for salinity control at the field scale. However, applying these models at the regional scale presents challenges due to the difficulty of obtaining observational data to calibrate and validate models that involve soil water-salt transport processes and crop growth. The rapid development of modern large-scale irrigation districts has changed the spatiotemporal distribution of soil water-salt transport. However, the continuous improvements of the model and the rapid development of computer technology have provided possibilities for investigating the spatiotempo-ral evolution mechanisms of water-salt transport in large-scale irrigation districts. Future models should focus on strengthening the soil water-salt transport mechanisms based on ecological safety. It is recommended to develop a multi-process water-salt transport model that is coupled with a climate model or an economic model. [ABSTRACT FROM AUTHOR]

Simulating river–lake systems while accounting for climate change and human activity is challenging, particularly when dealing with the complex interactions between rivers and lakes. In this study, we developed an integrated model coupling long short-term memory (LSTM), a two-dimensional hydrodynamic model (NewFlood), and the time-variant gain model (TVGM). We applied and validated this model using the Yangtze River–Poyang Lake system. The coupled model accurately and efficiently simulated the water levels, streamflow, and water exchange between the Yangtze River and Poyang Lake. The average Nash-Sutcliffe efficiency coefficient of the coupled model reached 0.92, indicating high simulation accuracy. The coupled model holistically accounts for the impacts of climate change and human activities on the spatiotemporal dynamics of river–lake systems, providing a robust scientific basis for quantifying the relative contributions of different factors influencing river–lake interactions. [ABSTRACT FROM AUTHOR]

Warmer temperatures significantly influence crop yields, which are a critical determinant of food supply and human well-being. In this study, a probabilistic approach based on bivariate copula models was used to investigate the dependence (described by joint distribution) between crop yield and growing season temperature (TGS) in the major producing provinces of China for three staple crops (i.e., rice, wheat, and maize). Based on the outputs of 12 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under Shared Socioeconomic Pathway 5–8.5, the probability of yield reduction under 1.5 °C and 2 °C global warming was estimated, which has great implications for agricultural risk management. Results showed that yield response to TGS varied with crop and region, with the most vulnerable being rice in Sichuan, wheat in Sichuan and Gansu, and maize in Shandong, Liaoning, Jilin, Nei Mongol, Shanxi, and Hebei. Among the selected five copulas, Archimedean/elliptical copulas were more suitable to describe the joint distribution between TGS and yield in most rice-/maize-producing provinces. The probability of yield reduction was greater in vulnerable provinces than in non-vulnerable provinces, with maize facing a higher risk of warming-driven yield loss than rice and wheat. Compared to the 1.5 °C global warming, an additional 0.5 °C warming would increase the yield loss risk in vulnerable provinces by 2–17%, 1–16%, and 3–17% for rice, wheat, and maize, respectively. The copula-based model proved to be an effective tool to provide probabilistic estimates of yield reduction due to warming and can be applied to other crops and regions. The results of this study demonstrated the importance of keeping global warming within 1.5 °C to mitigate the yield loss risk and optimize agricultural decision-making in vulnerable regions. [ABSTRACT FROM AUTHOR]