Diatoms constitute an important part of the phytoplankton community in lakes and reservoirs and play a significant role in regulating ecological balance. Danjiangkou Reservoir is the water source area of the middle route of China’s South-to-North Water Diversion project. In order to explore the spatial and temporal distribution and know the governing factors of the diatom community, 18srRNA sequencing was carried out from seven sampling sites of the reservoir. At the same time, the concentration of nutrients present in the collected sample water was also determined. The results showed that a total of 51 genera and 96 species were thriving the community of diatoms in Danjiangkou Reservoir. Discostella was dominant in summer and autumn, accounting for 98.84% and 62.71% of the diatom abundance, respectively. Aulacoseira was dominant in spring and winter, accounting for 60.62% and 60.90%, respectively. Discostella and Aulacoseira showed significant differences in seasonal variation (p < 0.05). The colinear network of diatoms changed significantly with the seasons, mainly consisting of Aulacoseira, Discostella, and Stephanodiscus. RDA redundancy analysis showed that water temperature (WT), total nitrogen (TN), NH₄⁺-N, pH, and electrical conductivity (Cond) were the main environmental factors driving the changes in diatom community structure.

Drought and flood catastrophes threaten water security in the Yellow River Basin, yet prediction is hampered by sparse extreme event records. This study couples catastrophe series extraction via GM(1,1), Weibull extreme value modeling, and bootstrap uncertainty quantification, applying it to 74 years (1951–2024) of annual precipitation in Zhengzhou, China. Percentile thresholds of 410 mm (drought) and 864.6 mm (extreme precipitation) yield five drought and seven extreme precipitation catastrophe years. Both Weibull fits pass three goodness-of-fit tests, whereas the GM(1,1) benchmark fails for the flood series (mean error 21.5% vs. 3.31% for drought). The 0.9 quantile recurrence times (17.8 and 22.9 years) project the next drought catastrophe around 2031 (90% bootstrap window 2025–2033) and the next extreme precipitation catastrophe around 2044 (2034–2047); conditional residual life forecasts shift these by only 1–2 years (to 2033 and 2045, respectively). Two truncated record hindcasts (2013 drought; 2003 flood) fell within the predicted windows, and threshold perturbation and alternative distribution analyses support their robustness. Divergent shape parameters (k = 2.47 vs. 1.29) reveal accelerating drought but decelerating extreme precipitation hazard, providing probabilistic lead times for disaster risk management in data-scarce regions.

Urban waterlogging is increasingly a test of built environment resilience; cities with similar rainfall can suffer very different losses depending on their drainage and flood control infrastructure. This study assesses the waterlogging risk for Kaifeng, Shangqiu, and Zhoukou (Eastern Henan, China) over 2015–2024 using a 15-indicator Hazard–Environment–Vulnerability–Resistance (HEVR) system, in which resistance indicators are reverse-coded so that a higher resistance expectation (R − Ex) denotes a larger capacity deficit. Subjective AHP weights and objective entropy weights are reconciled by an improved game-theoretic combination method (ICWGT) with reliability-proportional coefficients (0.516:0.484), and cloud model aggregation reports both risk position and classification dispersion. The mean risk gradient is Shangqiu (Ex = 5.252) > Kaifeng (5.023) > Zhoukou (3.797); high-risk years occur only in Kaifeng (2015 and 2018) and Shangqiu (2018). Contribution decomposition identifies Kaifeng as the largest resistance-deficit case (R − Ex = 7.174; 37.6% of its total contribution), and first-order screening indicates that closing this deficit to Shangqiu’s level would lower its mean Ex by 0.388. Machine learning classifiers serve strictly as a consistency check (SVM-RBF reproduces 83.3% of labels), supporting internal learnability rather than independent validation. The framework provides an interpretable way to prioritize drainage and flood control investment in the built environment.

To further explore the research status and progress of power lines in the field of resilience assessment and disaster warning, a visual analysis of power lines in the past 20 years was conducted based on Web of Science core database. The results showed that: The annual number of published papers in the research field indicated a gradual upward trend, and the research hotspots mainly included the resilience, optimization, model, microgrid, power and mechanical properties of transmission lines, etc. The keyword emergence shows that it pays attention to the traditional field of transmission lines in the early stage, and gradually pays attention to the overall performance improvement, disaster resistance and stability of the lines. And further expand to the transmission system of intelligence, automation, microstructure and efficient management. The most prolific author is Shahidehpour of the University of Chicago. In the collinear analysis of countries and institutions, China ranked first with 411 articles, followed by the United States with 299 articles, but China was lower than the United States in the number of citations. Chinese institutions accounted for 70 percent of the top 10, with Chongqing University and Xi 'an Jiaotong University Posting the highest number of articles in the field. The top three authors of highly cited papers are Shakhatreh of Jordan, Ma of the United States and Liu of China. These analysis results are helpful for scientific researchers to comprehensively understand the development trend and frontier hot spots in the research field of power line resilience assessment and disaster warning, and provide reference for the construction and development of power lines.

Timely, differentiated distribution of emergency materials reduces flood losses and supports resilient, sustainable cities, and yet demand urgency is rarely quantified jointly with routing. We propose an integrated framework combining game-theoretic AHP–CRITIC weighting, grey relational TOPSIS, exact Fisher–Jenks partitioning, and demand-driven ant colony routing applied to Jiaozuo’s July 2021 “7·20” flood in China. Demand urgency indices for eleven areas span 0.3064–0.6020 and are divided by exact Fisher–Jenks optimization into four grades mapping onto China’s four-tier emergency response system; the grades are robust across weighting schemes (identical under CRITIC-only and equal weights, with only two one-grade shifts under AHP-only weights) and stable under bootstrap perturbation. The routing model incorporates an edge risk exposure term; the optimal route trades an 8.0% increase in adjusted distance for a 6.1% reduction in urgency-weighted delivery time (F* = 235.8774), meeting the local 6 h first-wave dispatch target. At a matched evaluation budget, the improved and standard ant colony algorithms tie in solution quality, and both significantly outperform the genetic and particle swarm algorithms (p < 0.001). Robustness holds across weighting schemes, detour factors, and bootstrap perturbations, supporting resource-efficient, disaster-resilient emergency logistics (SDG 11.5).

Post-disaster research on the July 2021 (“7·20”) Henan flood has concentrated on the megacity Zhengzhou, leaving six severely hit mid-sized cities unassessed. This study assesses urban flood risk for Anyang, Hebi, Xinxiang, Jiaozuo, Puyang, and Jiyuan over 2018–2024 (42 city-year samples) on a 12-indicator Hazard–Exposure–Sensitivity–Adaptive capacity panel, objectively weighted (CRITIC × entropy, game-theory combination) and graded by set pair analysis. The design prioritizes verification over novelty: indicators survive first-difference collinearity screening (16 to 12); official disaster data are reserved as external validation criteria; results are stress-tested by seven robustness scenarios (including a threshold-free TOPSIS benchmark) and a cloud-model operator check reproducing the city ranking (ρ = 0.975). Exposure, not hazard, structures the ranking—Xinxiang highest (0.565) and Jiyuan lowest (0.446). The 2021 shock raised risk in all six cities (+0.027 to +0.087), and risk proves sticky: Xinxiang and Jiyuan had not returned to pre-disaster levels by 2024. Obstacle profiles are city-specific and exposure-led, with drainage shortfall acute in the two sticky cities. On a three-city validation sample, the panel reproduces official hazard and affected-population rankings exactly (Spearman ρ = 1.0; illustrative given n = 3). Indicator-panel screening offers mid-sized city clusters a data-light route to flood-risk triage beyond megacity attention.