The operation of flood retarding areas does not attract much attention, although they are important components of flood control systems. Poor operation of such areas restricts not only the socio-economic development of the flood retarding area, but also limits scientific flood control options. As the second-largest flood retarding area in China, with more than 2000 km² and 300 polders, the Hongze Lake vicinity was taken as a case study of graded flood retarding. A one and two-dimensional coupled hydrodynamic model was established to simulate flood routing in the Hongze Lake area. Fifteen different schemes involving different flood magnitudes and flood retarding operations were simulated. The results show that (1) having a flood retarding area is essential; and (2) the “graded flood retarding” scheme is superior to “no grading flood retarding” scheme; and (3) a “two-grade flood retarding” scheme is recommended.

The Huaihe River Basin is particularly vulnerable to climate change. This paper first evaluated interpolation methods for different meteorological elements, followed by an assessment of the simulation performance of various Coupled Model Intercomparison Project 5 (CMIP5) Global Climate Models (GCMs) for these elements. We then applied the Improved Quantile Mapping (IQM) method for bias correction of the GCMs. Finally, we analyzed the characteristics of future climate change in the Huaihe River Basin. The results show the following: (1) The radial basis function interpolation method is the most effective for rainfall, while Kriging performs best for air temperature. (2) The HadGEM2-AO model provides the most accurate rainfall simulations, MIROC-ESM best simulates maximum air temperature, and HadGEM2-ES is most effective for minimum air temperature. (3) The IQM method outperforms other approaches for bias correction of climate variables in the basin. (4) Future projections show an increase in both rainfall and air temperature, with more pronounced rises under the RCP8.5 scenario. Additionally, rainfall and maximum air temperature show considerable spatial variation across emission scenarios, while minimum air temperature consistently exhibits an upward trend.

To advance the modernization of power systems, the role of hydropower has transitioned from mere energy generation to serving as both a primary energy provider and a critical flexibility resource. However, conventional day-ahead scheduling methodologies for cascaded hydropower often prove inadequate in mitigating the short-term power imbalances induced by renewable energy variability, primarily due to an oversimplification of operational constraints. To fully harness the regulatory potential of hydropower, this paper proposes an optimal day-ahead scheduling strategy for a cascade hydro-wind-solar-thermal-storage system incorporating hybrid pumped storage (HPS). First, leveraging the operational characteristics of HPS, this study evaluates its capacity for renewable energy accommodation and identifies key challenges in power balancing from the perspectives of water resource management and energy dispatch. Second, a collaborative optimization framework is established. By introducing the concept of an “allowable reservoir water level fluctuation range” within the day-ahead dispatch cycle, the framework constrains the deviation between the actual end-of-period water level and its predefined target, thereby ensuring strict adherence to medium-to-long-term operational boundaries. Finally, case studies conducted on a modified IEEE 33-node system demonstrate that the integration of HPS significantly enhances system regulation capabilities. The results confirm that the proposed strategy contributes to secure, economical, and low-carbon grid operation while maintaining reservoir sustainability.

Accurate short-term forecasts of meteorological parameters remain challenging in mountainous small watersheds because sparse observations and global background fields do not adequately capture rapid local water vapor variations. Previous studies indicate that assimilating GNSS-derived PWV can improve regional-scale forecasts of precipitation and near-surface meteorological variables. However, its incremental value in mountainous small watersheds remains uncertain. Complex terrain, sparse local observations, and rapid water vapor variability may limit the transferability of findings obtained over larger domains. To address this setting-specific gap, we assess the impact of GNSS-derived PWV assimilation on short-term numerical forecasts for the Shahe watershed and its surrounding area in Hanzhong, China. Using the Weather Research and Forecasting Data Assimilation (WRFDA) system, the MET experiment assimilated conventional meteorological observations from four GNSS meteorological (GNSS-Met) stations, whereas MET + PWV additionally assimilated GNSS-derived PWV from the same four stations. The 129 CMA meteorological stations were reserved exclusively for independent forecast verification. During the selected precipitation period, adding GNSS-derived PWV reduces hourly precipitation RMSE by 4.7% compared with assimilating conventional meteorological observations alone. It also increases the threat score (TS) and probability of detection (POD) by 3.8% and 5.0%, respectively. Moreover, adding GNSS-derived PWV reduces the mean RMSEs of air temperature and relative humidity by 10.2% and 8.0%, respectively, while the corresponding reduction in surface pressure RMSE is 2.9%. During the selected non-precipitation period, adding GNSS-derived PWV to conventional meteorological-observation assimilation reduces the 1–6 h RMSEs of air temperature, relative humidity, and surface pressure by 6.0–8.9%, 8.3–9.8%, and 4.2–4.3%, respectively. For the selected precipitation and non-precipitation cases, the results indicate that adding GNSS-derived PWV to conventional meteorological observations from the same four GNSS-Met stations provides an additional local moisture constraint and improves short-term forecasts of multiple meteorological parameters in the Shahe watershed and its surrounding area.