Water resources are important for supporting regional economic development. A scientific and reasonable evaluation of the carrying capacity of water resources is of high significance to regional sustainable development. Zhangjiakou City is China’s first capital water conservation functional area, which plays a role in ensuring the safety of water resources and ecological environment in the capital. Considering Chicheng County, Zhangjiakou City, as a typical area, its water resource carrying capacity in the current year (2018) and the plan year (2025) is evaluated by constructing a water resource carrying capacity evaluation index system for the county, determining the threshold of the bearing capacity evaluation index, and using the single-factor evaluation method and cloud theory method. In addition, the main factors affecting the water resource carrying capacity of Chicheng County are analyzed. The results show that: Based on the single factor evaluation results, the critical state of water resources carrying capacity was evaluated comprehensively, and the factor carrying capacity of total water consumption in the predicted planning level year (38.0603 in normal water year and 41.3403 in dry water year) does not exceed the total water consumption index (51.14). Furthermore, The water resources carrying capacity level of the present level year (.45) and the planned level year (.46) belong to the third level of the evaluation index system. There is further scope for the carrying capacity of water resources in Chicheng County to satisfy its economic and social development. However, it is urgent to solve the problems affecting the water quality elements of rivers, such as heavy river water pollution, low sewage treatment capacity, and weak pollution monitoring and supervision. It is necessary to optimize the industrial economic structure of Chicheng County, accelerate the improvement of water resources management system and mechanism, and ensure the long-term safety of water resources and ecological environment in the capital.

Non-point source pollution is widely considered a serious threat to drinking water. Eutrophication in Chinese watershed is mainly due to nitrogen and phosphorus output from agricultural source. Taihu Lake is a typical eutrophic lake in China, a basin representative for the study of the temporal-spatial characteristics of pollution loading of nitrogen and phosphorus to provide scientific basis for reasonable estimation and targeted control measures of nitrogen and phosphorus loss. Based on data from nitrogen and phosphorus loss in agricultural land, livestock breeding, domestic discharge and aquaculture, this study calculated the levels of nitrogen and phosphorus comprehensive loss risk for each pollution source. Using the superposition of ArcGIS raster data, we also described the spatial distribution of nitrogen and phosphorus comprehensive loss risk by the formula of comprehensive loss risk. The results showed that critical risk areas of nitrogen and phosphorus loss mainly originated from livestock breeding and agricultural land during flood period in Tiaoxi watershed. Agricultural land and livestock breeding sources formed major parts of nitrogen loss, accounting for 30.85% and 36.18%, respectively, while phosphorus loss mainly originated from livestock breeding (56.28%). During non-flood period, integrated management of livestock breeding and domestic discharge requires much attention to control nitrogen and phosphorus loss in the critical risk area. Finally, it is of great practical significance to propose spatial-temporal targeted measurements to control nitrogen and phosphorus pollution in watershed for various periods and different areas.

Small hydropower substituting fuel (SHSF) is an ecological environment protection program to improve regional ecosystems and alleviate poverty. However, the sustainability of SHSF programs remains controversial due to lingering doubts about its potential for socioeconomic development and its environmental impacts. The sustainability of SHSF was examined based on field investigations and household questionnaire surveys. The results were as follows: (1) Biomass of SHSF protected masson pine (Pinus massoniana) and weeping cypress (Platycladus orientalis) plantations were 11.06 t·ha⁻¹ and 7.15 t·ha⁻¹ higher than unprotected plantations, respectively. Furthermore, the differences in ecosystem biomass were mainly derived from arbor biomass. While the energy conversion efficiency based on field investigations was merely 1.28 kg (kWh)⁻¹, which was only 64% of the empirical value and 54% of the guideline for accounting for the ecological benefit of small hydropower substituting fuel. (2) Households’ total income in SHSF villages was higher than in households with access to a hydropower plant but no substituting fuel or households with no hydropower plant. (3) Most of the households had a positive attitude towards SHSF because of its cheaper electricity and associated ecological environmental improvements. Overall, our results suggest optimistic and sustainable prospects for the SHSF program; however, continued education and policy communications are needed to sustain program success.

Most slope collapse accidents are indicated by certain signs before their occurrence, and unnecessary losses can be avoided by predicting slope deformation. However, the early warning signs of slope deformation are often misjudged. It is necessary to establish a method to determine the appropriate early warning signs in sliding thresholds. Here, to better understand the impact of different scales on the early warning signs of sliding thresholds, we used the Fisher optimal segmentation method to establish the early warning signs of a sliding threshold model based on deformation speed and deformation acceleration at different spatial scales. Our results indicated that the accuracy of the early warning signs of sliding thresholds at the surface scale was the highest. Among them, the early warning thresholds of the blue, yellow, orange, and red level on a small scale were 369.31 mm, 428.96 mm, 448.41 mm, and 923.7 mm, respectively. The evaluation accuracy of disaster non-occurrence and occurrence was 93.25% and 92.41%, respectively. The early warning thresholds of the blue, yellow, orange, and red level on a large scale were 980.11 mm, 1038.16 mm, 2164.63 mm, and 9492.75 mm, respectively. The evaluation accuracy of disaster non-occurrence and occurrence was 97.22% and 97.44%, respectively. Therefore, it is necessary to choose deformation at the surface scale with a large scale as the sliding threshold. Our results effectively solve the problem of misjudgment of the early warning signs of slope collapse, which is of great significance for ensuring the safe operation of water conservation projects and improving the slope deformation warning capability.

Slope collapse is one of the most severe natural disaster threats, and accurately predicting slope deformation is important to avoid the occurrence of disaster. However, the single prediction model has some problems, such as poor stability, lower accuracy and data fluctuation. Obviously, it is necessary to establish a combination model to accurately predict slope deformation. Here, we used the GFW-Fisher optimal segmentation method to establish a multi-scale prediction combination model. Our results indicated that the determination coefficient of linear combination model, weighted geometric average model, and weighted harmonic average model was the highest at the surface spatial scale with a large scale, and their determination coefficients were 0.95, 0.95, and 0.96, respectively. Meanwhile, RMSE, MAE and Relative error were used as indicators to evaluate accuracy and the evaluation accuracy of the weighted harmonic average model was the most obvious, with an accuracy of 5.57%, 3.11% and 3.98%, respectively. Therefore, it is necessary to choose the weighted harmonic average model at the surface scale with a large scale as the slope deformation prediction combination model. Meanwhile, our results effectively solve the problems of the prediction results caused by the single model and data fluctuation and provide a reference for the prediction of slope deformation.

Flood disaster risk assessment serves as an important foundation for formulating regional sustainable development strategies. This study establishes a risk assessment model for flood disasters in small and medium-sized rivers based on a theoretical framework integrating Geographic Information Systems (GIS), the Delphi method, and Bayesian networks (Delphi–BNs). An indicator system for the assessment was developed from three dimensions: hazard, vulnerability, and exposure. Hazard is represented by flood inundation area and depth; vulnerability is indicated by population distribution and economic layout; and exposure is reflected by road accessibility. By constructing a GIS-based Bayesian network and employing the Delphi method to create a probabilistic and spatially explicit model, this approach quantifies various sources of uncertainty in the assessment process, enabling a probabilistic expression of risk. Based on the risk assessment results, a stratified, phased flood emergency rescue and personnel transfer plan was established, designating extremely high-risk areas as the core zones for the first phase of personnel transfer, high-risk areas as the second-phase rescue zones, and medium-risk areas as the third-phase rescue zones, thereby providing clear operational guidance for flood emergency response in the basin. The Delphi–BN assessment framework developed in this study focuses on the core elements of flood disaster risk formation, organically integrates expert experience with spatial big data, and effectively overcomes the limitations of traditional assessment methods, such as strong subjectivity, insufficient accuracy, and poor quantification. It achieves a refined and quantitative assessment of flood risk in small and medium-sized river basins in semi-arid regions. The outcomes of this research contribute to a clearer understanding of both the driving mechanisms and the spatial patterns of regional flood risk. Furthermore, they establish a scientifically credible and operationally relevant foundation for key disaster-response decisions, encompassing timely emergency actions, phased population transfers, and the optimized deployment of limited emergency resources.