Shixiang Gu’s research while affiliated with Wuhan University and other places

Driven by population growth and economic development, the demand for water resources continues to increase worldwide, leading to regional and seasonal water scarcity in many countries. Accurate assessment of water scarcity risks is essential for effective water resource risk management and allocation, depending on the continuous development and innovation of water scarcity risk evaluation methods. This study reviews the historical development of these methods, analyzes their trends, and summarizes the theoretical foundations, implementation processes, application scenarios, and limitations, providing insights for future research directions. Initially, we employ bibliometric statistics and keyword clustering analysis to investigate the spatiotemporal distribution of relevant studies and identify focal points and hotspots, uncovering relationships between publication numbers and factors such as national attention, population, and financial investment. We innovatively categorize primary evaluation methods into six types: single index method, indicator system evaluation, stochastic simulation, artificial intelligence, remote sensing and GIS, and virtual water economics. Theoretical foundations, implementation processes, and algorithm principles of each category are summarized, and application scenarios and limitations are discussed, facilitating a deeper understanding and expansion for readers. Finally, we explore and prospect key issues, including assessment and prediction of water scarcity risks under multi-scenario coupling, differences and accuracy of water scarcity risk assessments at various spatiotemporal scales, rural water scarcity risk evaluation, and integration of high-resolution remote sensing with drought limit water levels for water scarcity risk assessment, aiming to provide new directions and perspectives for future research.

Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R² of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion.

This study investigates waterlogging disasters in winter wheat using the Agricultural Production Systems Simulator (APSIM) model. This research explores the effects of soil hypoxia on wheat root systems and the tolerance of wheat at different growth stages to waterlogging, proposing a model to quantify the degree of waterlogging in wheat. Remote sensing data on soil moisture and wheat distribution are utilized to establish a monitoring system for waterlogging disasters specific to winter wheat. The analysis focused on affected areas in Bengbu and Jingzhou. Experimental results from 2017 to 2022 indicate that the predominant levels of waterlogging disasters in Bengbu and Jingzhou were moderate and mild, with the proportion of mild waterlogging ranging from 30.1% to 39.3% and moderate waterlogging from 14.8% to 25.6%. A combined analysis of multi-source remote sensing data reveals the key roles of precipitation, evapotranspiration, and altitude in waterlogging disasters. This study highlights regional disparities in the distribution of waterlogging disaster risks, providing new strategies and tools for precise assessment of waterlogging disasters.

Accurate assessment and prediction of water shortage risk are essential prerequisites for the rational allocation and risk management of water resources. However, previous water shortage risk assessment models based on copulas have strict requirements for data distribution, making them unsuitable for extreme conditions such as insufficient data volume and indeterminate distribution shapes. These limitations restrict the applicability of the models and result in lower evaluation accuracy. To address these issues, this paper proposes a water shortage risk assessment model based on kernel density estimation (KDE) and copula functions. This approach not only enhances the robustness and stability of the model but also improves its prediction accuracy. The methodology involves initially utilizing kernel density estimation to quantify the random uncertainties in water supply and demand based on historical statistical data, thereby calculating their respective marginal probability distributions. Subsequently, copula functions are employed to quantify the coupled interdependence between water supply and demand based on these marginal probability distributions, thereby computing the joint probability distribution. Ultimately, the water shortage risk is evaluated based on potential loss rates and occurrence probabilities. This proposed model is applied to assess the water shortage risk of the Yuxi water receiving area in the Central Yunnan Water Diversion Project, and compared with existing models through experimental contrasts. The experimental results demonstrate that the model exhibits evident advantages in terms of robustness, stability, and evaluation accuracy, with a rejection rate of 0 for the null hypothesis of edge probability fitting and a smaller deviation in joint probability fitting compared to the most outstanding model in the field. These findings indicate that the model presented in this paper is capable of adapting to non-ideal scenarios and extreme climatic conditions for water shortage risk assessment, providing reliable prediction outcomes even under extreme circumstances. Therefore, it can serve as a valuable reference and source of inspiration for related engineering applications and technical research.

Water, soil, and heat are strategic supporting elements for human survival and social development. The degree of matching between human-land-water-heat elements directly influences the sustainable development of a region. However, the current evaluation of the matching of human-land-water-heat elements overlooks the influence of elevation factors on the matching results, especially evident in mountainous areas. Taking the Yunnan Plateau with distinctive mountainous features as the research subject, divided into 11 elevation ranges, the Lorenz Gini coefficient, asymmetry coefficient, matching distance, and imbalance index are used to assess the spatial matching and balance of human-land-water-heat elements. A projection tracing model is employed to analyze its water resource carrying capacity. Analyses revealed that the Gini coefficient of monthly precipitation from the 1950s to 2022 on the Yunnan Plateau increases with increasing latitude, whereas the correlation with elevation is notably lower. The asymmetry coefficient increases gradually from west to east with change in longitude. The mismatch of the human–land–water–heat system in regions at different elevations is in the order 1800–2000 m > 2000–2200 m > 1400–1600 m > 800 m > other areas. The matching of the human–land–water–heat system in different wet–dry years and seasons also fluctuates with elevation, resulting in serious seasonal drought and water shortage problems in mountainous areas with elevations of 1200–1600, 1800–2000 m, and >2600 m. The spatial equilibrium of temperature and precipitation in regions of different elevations is best, followed by that of cultivated land, while that of the population is the worst. The Gini coefficients for different water cycle processes of precipitation, surface runoff, and regulating storage capacity for water supply continue to increase. Specifically, the Gini coefficient of industrial water supply is the highest, reaching 0.576, and that of agricultural irrigation is the lowest (0.424). Through artificial regulation of lake and reservoir water, seasonal changes in the demand for agricultural irrigation water are offset to achieve a demand–supply balance and matching of land and water resources. The water resource capacity of different elevation ranges is evenly underloaded. However, the potential of the water resource capacity varies obviously with elevation in the order 2000–2200 m < 1800–2000 m < 1600–8000 m < 1400–1600 m < other areas. It appears that the greater the human–land–water–heat system mismatch, the smaller the regional potential of the water resource capacity.

The technical research on determining the drought limit water level can be used as an important basis for starting the emergency response of drought resistance in the basin and guiding the drought resistance scheduling of water conservancy projects. When the concept of drought limit water level was first proposed, the main research object was reservoirs, and the method for determining the lake drought limit water level was not established. Referring to the calculation method of reservoir drought limit water level, the drought limit water level is used as a single warning indicator throughout the year, which lacks graded and staged standards, and also lacks rationality and effectiveness in practical application. Therefore, this article has improved the concept of lake drought limit water level (flow). Under different degrees of drought and water use patterns during the drought period, combined with the characteristics of lake water inflow, considering the factors such as ecology, water supply, and demand, lake inflow, evapotranspiration loss, a graded and staged standard of lake drought limit water level has been developed. For different types of lakes, a general method for determining the lake’s graded and staged drought limit water level has been established. The SCSSA-Elman neural network is used to construct the medium and long-term water inflow prediction model for lakes, and the calculation results of this model are used for the warning and dynamic control analysis of the lake drought limit water level. The application of this method has the characteristics of strong applicability and high reliability. Finally, the determination method and dynamic control method of the lake’s graded and staged drought limit water level have been successfully applied at Dianchi Lake in Yunnan.

Lake water level changes show randomness and the complexity of basin hydrological simulation and lake water level response. We constructed a vine copula model to simulate and predict lake water level that incorporated rolling decisions and real-time correction of prediction results. The model was applied to predict the long- and short-term water levels in Erhai Lake on the Yun-gui Plateau, southwest China. The results showed that (1) the predicted daily water levels (with ME=0.02~0.09, RMSE=0.02~0.024, NSE=0.99, and IA=0.99) were more accurate than the predicted monthly water levels (with the ME=0.039~0.444, RMSE=0.194~0.279, NSE=0.913~0.958, and IA=0.977~0.989), and the accuracy of the predictions improved as the number of variables increased. (2) The vine copula model outperformed the back-propagation neural network and support vector regression models, and, of the three model types, gave the best estimate of the nonlinear relationships between the predicted water level and climatic factors, especially in the wet season (May to October). (3) The prediction accuracy of the vine copula model was lower for small sample sizes and when there was a lack of runoff data. By improving the analysis of the model’s errors, the percentages of the relative errors of the prediction accuracy less than 5%, 10%, 15%, and 20% increased to 70%, 83%, 95%, and 98%, respectively.

Poultry and livestock farming wastewater is one of the main sources of agricultural non-point source pollution. Using reclaimed poultry and livestock farming wastewater for rice irrigation can not only provide nutrition for rice, but also reduce agricultural water consumption, and also further control non-point source pollution through the self-purification of rice ecosystem. It has been considered to be environmentally friendly and reliable for poultry and livestock farming wastewater treatment. However, the effect of reclaimed water irrigation on rice yield and quality is still unclear. In order to explore the effects of reclaimed water irrigation on rice yield and quality, we conducted a rice reclaimed irrigation experiment in the Erhai lake basin in 2017. The rice yield and quality were compared among different irrigation modes, different irrigation water quality and different fertilization schemes of reclaimed water. The results showed that the yield of W1F2, under intermittent irrigation with reclaimed water, was the highest, reaching 11.7t/hm². The differences of rice yield and its components between different irrigation and fertilization methods were not significant. The yield and its components of rice under different irrigation and fertilization treatments were not significant. In terms of yield, the intermittent irrigation was 7.6% higher than that of flood irrigation. The amount of fertilizer applied by intermittent irrigation of reclaimed water was 7.0% less than that of clear water, and the yield increased by 3.5%. Correspondingly, the amount of fertilization of flood irrigation of reclaimed water is equivalent to that of clear water, which increases yield by 1.9%. In summary, the effect of reclaimed water irrigation on yield increase is obvious. There were significant differences in brown rice rate and chalky grain rate between different irrigation modes, which showed that the brown rice rate of intermittently irrigated rice was 0.7% smaller than that of flooded rice, and the white grain of intermittently irrigated rice was 45% larger than that of flooded irrigation. Intermittent irrigation has an adverse effect on the processing quality and appearance quality of rice. There were significant differences in brown rice rate, whole milled rice rate, gel consistency and depletion value under different irrigation water quality. The results showed that the brown rice rate and the depletion value of reclaimed water irrigation were smaller than those of clear water, and the whole milled rice rate and gel consistency were larger than that of clear water. Regularity, reclaimed water irrigated rice processing quality and cooking taste quality is better. In terms of nutritional quality of rice, the crude protein content in flooding treatment was 4.2% higher than that in intermittent irrigation, and the irrigation treatment of reclaimed water was 13.8% higher than that in clear water irrigation.