In the quest to scientifically develop power systems increasingly reliant on renewable energy sources, the potential and temporal complementarity of wind and solar power in China’s northwestern provinces necessitated a systematic assessment. Using ERA5 reanalysis data for wind speed and solar irradiance, an evaluation was carried out to determine the potential and spatial distribution of wind and solar power across these provinces. Land use types and terrestrial surface slopes were considered in gauging this potential. Theoretical wind and solar power outputs were then compared to understand their complementarity on annual, monthly, and hourly temporal scales. This exploration utilized methodologies including rank correlation coefficients, crossover frequency analysis, and standard deviation complementarity rates. Areas such as the Tarim Basin, Jungar Basin, and the northeastern part of Xinjiang, northwestern Qinghai, and northern Gansu were identified as having significant wind and solar power potential, with wind power densities reaching as high as 600 W/m² and solar irradiance surpassing 2000 kWh/m². In these energy-rich areas, the distinct complementarity between theoretical wind and solar outputs was discerned. On an annual scale, the complementarity appeared weakest, with only 7.48% of the combined provinces’ area showing medium-level complementarity. On a monthly scale, conversely, a pronounced complementarity was displayed, especially during the March–May and October–November periods. When evaluated on an hourly basis, an impressive 63.63% of the total output duration exhibited complementary characteristics.

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.

Quantitative risk assessment of flash floods is crucial for developing disaster prevention and mitigation strategies. This study developed a refined framework that innovatively integrates field-validated data from Chongqing’s flash flood disaster investigation project with AHP, factor analysis, and cluster analysis to quantify hazard, vulnerability, resistance, and risk indicators at a 30 m grid. Unlike existing coarse-scale assessments that rely on generic indicators, this hybrid model, calibrated by observed disaster evidence, significantly enhanced the local relevance and reliability of risk zoning. The validity of this framework was confirmed through validation against objective weighting methods and historical flash flood locations. The results indicated that the risk value of flash floods in Chongqing was between 0.24 and 0.69, with extremely high-risk and high-risk zones covering 42,388 km² (51.47%) of the study area. This accurately identifies areas at high risk of flash floods and provides a basis for government decision-making regarding priority areas for disaster risk reduction investments. Verification showed that 83.44% of historical disaster points fall within medium-risk or above zones, confirming the framework’s accuracy in identifying flood-prone hotspots and providing actionable support for targeted early warning and resource allocation.

Quantitative risk assessment of flash flood is crucial for the development of disaster prevention and mitigation strategies. Based on investigation and evaluation of flash flood disaster and the natural geographic conditions, a flash flood risk assessment framework was constructed considering hazard, vulnerability and resilience. Chongqing, known as the mountain city, was selected as a case study. Results indicate that the risk value of flash flood in Chongqing is between 0.25 and 0.66. The areas of extremely high-risk zone, high-risk zone, medium-risk zone and low-risk zone in Chongqing are 1,707,200, 2,773,800, 2,570,100 and 1,183,700 square kilometers, respectively, which the percentage of area in high risk and above areas is as high as 54.41%. Extremely high-risk areas are mainly concentrated in Wuxi in the northeast of Chongqing, Youyang in the southeast of Chongqing, and Nanan, Rongchang and Hechuan districts in the main urban metropolitan area, while high-risk areas are mainly located in Chengkou in the northeast of Chongqing, Pengshui and Shizhu in the southeast of Chongqing, and Jiangbei and Tongnan districts in the main urban metropolitan area. It is worth noting that extremely high-risk zones are distributed more than 50% in Dadukou, Hechuan, Nanan, Rongchang, Shapingba, Wuxi and Youyang districts, and high-risk zones are distributed more than 50% in Chengkou, Jiangbei, Nanchuan, Pengshui, Shizhu, Tongnan and Yunyang districts, suggesting that these districts should be given more attention in flash flood disaster defense.