Ecological compensation has recently gained significant attention as an economic incentive promoting natural resource management. However, there remain several challenges to its application. A key issue is the lack of a method clearly define the standard of ecological compensation. This study established an accounting methodology for the ecological compensation standard for the water-receiving area in an inter-basin water diversion in China. Beijing, a major water-receiving area of the South-to-North Water Diversion Project, was used as an example of the application of this approach. First, the evaluation index of ecosystem service value of the water-receiving area was selected, then, emergy theory was used to calculate the increment of ecological service value based on the characteristics of each indicator. The ecological service value due to the project was calculated to be 3.898 billion RMB, while the willingness-to-pay by the public was estimated at 915 million RMB. Therefore, the increment of ecological service value was the highest standard of compensation, and the public’s willingness-to-pay was the lower limit of ecological compensation. The final compensation standard can be determined through negotiation between suppliers and beneficiaries under the leadership of relevant governments and water-transfer authorities. Thus, this study aimed to provide a scientific basis for the construction of diversified ecological compensation mechanisms and promote sustainable development of the region.

Predicting future drought conditions are crucial for effective disaster management. In this study, a machine learning framework is proposed to predict hydrological drought in the Huaihe River Basin, China. The Extreme Gradient Boosting (XGBoost) model is applied to predict four drought categories in 28 grid regions for one-month prediction, using 26 features for monthly and 18 for seasonal predictions. The framework also integrates the Shapley Additive Explanation (SHAP) variable importance index to infer drought prediction features. The model achieves 79.9 % accuracy in classifying droughts, with the Standard Precipitation Index (SPI) being the most influential feature. The SHAP values of SPI are 0.360, 0.261, 0.169, and 0.247 for spring, summer, autumn, and winter, respectively. Soil moisture content and evapotranspiration are particularly affected in spring and autumn, while large-scale climatic features are more significant in summer and winter. Overall, this study offers valuable decision support for regional drought management and water resource allocation.

Forecasting the occurrence of hydrological drought according to a forecasting system is an important disaster reduction strategy. In this paper, a new drought prediction model adapted to changing environments was constructed. Taking the Luanhe River basin in China as an example, first, nonstationarity analysis of hydrological sequences in the basin was carried out. Then, conditional distribution models with the human activity factor as an exogenous variable were constructed to forecast hydrological drought based on meteorological drought, and the results were compared with the traditional normal distribution model and conditional distribution model. Finally, a scoring mechanism was applied to evaluate the performance of the three drought forecasting models. The results showed that the runoff series of the Luanhe River basin from 1961 to 2010 were nonstationary; moreover, when human activities were not considered, the hydrological drought class tended to be the same as the meteorological drought class. The calculation results of the models involving HI as an exogenous variable were significantly different from the models that did not consider human activities. When the current drought class tended towards less severe or normal, the meteorological drought tended to turn into more severe hydrological drought with the increase in human index values. According to the scores of the three drought forecasting models, the conditional distribution models involving the human index can further improve the forecasting accuracy of drought in the Luanhe River basin.

Background: Zeolite (Z) is gradually used in rice production due to its holding ability for water and nutrients, but limited information is available on how its physiological function affects rice grain yield and quality under water stress. Methods: This study aimed to investigate the effect of Z application on rice physiological characteristics, dry matter and nitrogen accumulation, grain yield and quality under continuous flooding (CF) and alternate wetting and drying irrigation (AWD). Results: The results showed that, compared with CF, AWD reduced leaf SPAD, root bleeding intensity, aboveground dry matter and nitrogen accumulation, resulted in lower grain yield without Z application, but improved root–shoot ratio and root N accumulation. Z application increased dry matter and N accumulation, and subsequent grain yield by improving leaf SPAD and root bleeding intensity. Both AWD and Z application improved water use efficiency. AWD reduced head rice rate, chalky rice rate and chalkiness, but improved the taste value by increasing the breakdown and reducing the setback. Z application improved protein content, reduced breakdown and setback, but increased chalky rice rate and chalkiness. Conclusions: These results indicated that AWD and Z application could achieve several benefits including improved grain yield, grain quality and water use efficiency.

Investigating the processes governing drought propagation under a changing environment is essential for advancing drought early warning and reducing socio-economic risks. Currently, few studies have analyzed the effects of meteorological factors and watershed characteristics on drought propagation based on non-stationary drought indices. In this paper, the probabilities and thresholds of meteorological drought to hydrological drought propagation were calculated using the non-stationary drought index constructed using the Generalized Additive Model for Location, Scale, and Shape (GAMLSS) model and the Copula function to assess the influence of large-scale climatic indices, meteorological elements, and watershed characteristics on the propagation characteristics of seasonal droughts. The results showed that non-stationary drought indices that incorporate meteorological factors tended to have a better performance than standardized drought indices. Under the combined influence of large-scale climatic indices, temperature, specific humidity, and wind speed, the propagation probabilities became larger especially during spring and winter in the upstream and midstream regions of the Luanhe River Basin, China, with the propagation thresholds in winter significantly increases by 0.1–0.2. These mean that hydrologic droughts are more likely to be triggered. Furthermore, the spatial variability of drought propagation is further influenced by watershed characteristics, including the slope and leaf area index, which collectively alter runoff generation processes.

Predicting future drought conditions is crucial for effective disaster management. In this study, a machine learning framework is proposed to predict hydrological drought in the Huaihe River Basin, China. The interpretable Extreme Gradient Boosting (XGBoost) model is applied to forecast four drought categories in 28 grid regions, using 26 factors for monthly and 18 for seasonal predictions. The framework also integrates the Shapley Additive Explanation (SHAP) variable importance index to infer drought prediction factors. The model achieves 79.9 % accuracy in classifying droughts, with the Standard Precipitation Index (SPI) being the most influential factor. The SHAP values of SPI are 0.360, 0.261, 0.169, and 0.247 for spring, summer, autumn, and winter, respectively. Soil moisture content and evapotranspiration are particularly affected in spring and autumn, while large-scale climatic factors are more significant in summer and winter. Overall, this study offers valuable decision support for regional drought management and water resource allocation.