The traditional flood season division method is cumbersome. In order to make the flood season division elaborate, the Mann–Kendall and cumulative sum of rank difference (CSD) methods were used to detect the abrupt change year of precipitation (p) over the study area from 1969 to 2015. The year of change was determined to be 1995. Taking the 1995 year as a demarcation point of the data, the discriminant model and Fisher optimal partition method were applied for flood division, and a comparison of the results from the two approaches were compared. The discriminant model was found to perform slightly better than the Fisher approach. It was found that abrupt rainfall change has a certain influence on flood season division. The main flood season in the Zhangjia Zhuang reservoir during 1969–2015 was 16 days longer than during 1996–2015, but three days shorter than between 1969–1995. For the Zhangjia Zhuang Reservoir, the flood water level limit can increase up to 2 m according to the results of the flood season division and designed rainfall after abrupt change; in addition, the water storage capacity is 469 million m³ more than that of the traditional reservoir operation mode.

In this study, we conducted a comprehensive analysis of surface water area changes in 9235 small and medium-sized reservoirs across China from 1985 to 2021. Using Landsat and Sentinel-2 satellite data, our investigation delved into the spatiotemporal changes in these reservoirs and explored reservoir-based droughts. Using a robust algorithm, we examined the spatial and temporal patterns of surface water area (SWA) change on a national scale. While cumulative SWA remained stable at the national level, our analysis revealed diverse variations in individual catchments. To improve our understanding of reservoir-based hydrological drought, we introduced the Standardized Area Index (SAI). This index serves as a metric for quantifying drought severity and reveals a distinct north–south divide in China. The study shows that water-scarce northern regions experience prolonged and severe droughts, necessitating increased conservation efforts. Conversely, the water-rich southern region prioritizes increasing reservoir capacity. Our findings underscore the importance of small and medium-sized reservoirs in shaping China’s water resources landscape. Furthermore, this study provides valuable insights into the nuanced characteristics of droughts, facilitating the development of regionally tailored water management strategies.

Landslide susceptibility mapping (LSM) faces persistent challenges in defining representative stable samples as conventional random selection often includes unstable areas, introducing spatial bias and compromising model accuracy. To address this, we redefine the certainty factor (CF) method—traditionally for factor weighting—as a spatial screening tool for stable zone delineation and apply it to the tectonically active upper Jinsha River (937 km², southeastern Tibetan Plateau). Our approach first generates a preliminary susceptibility map via CF, using the natural breaks method to define low- and very low-susceptibility zones (CF < 0.1) as statistically stable regions. Non-landslide samples are exclusively selected from these zones for support vector machine (SVM) modeling with five-fold cross-validation. Key results: CF-guided sampling achieves training/testing AUC of 0.924/0.920, surpassing random sampling (0.882/0.878) by 4.8% and reducing ROC standard deviation by 32%. The final map shows 88.49% of known landslides concentrated in 25.70% of high/very high-susceptibility areas, aligning with geological controls (e.g., 92% of high-susceptibility units in soft lithologies within 500 m of faults). Despite using a simpler SVM, our framework outperforms advanced models (ANN: AUC, 0.890; RF: AUC, 0.870) in the same region, proving physical heuristic sample curation supersedes algorithmic complexity. This transferable framework embeds geological prior knowledge into machine learning, offering high-precision risk zoning for disaster mitigation in data-scarce mountainous regions.