The completeness of precipitation observation data is a crucial foundation for hydrological simulation, water resource analysis, and environmental assessment. Traditional data imputation methods suffer from poor adaptability, lack of precision, and limited model diversity. Rapid and accurate imputation using available data is a key challenge in precipitation monitoring. This study selected precipitation data from the Jiaojiang River basin in the southeastern Zhejiang Province of China from 1991 to 2020. The data were categorized based on various missing rates and scenarios, namely MCR (Missing Completely Random), MR (Missing Random), and MNR (Missing Not Random). Imputation of precipitation data was conducted using three types of Artificial Intelligence (AI) methods (Backpropagation Neural Network (BPNN), Random Forest (RF), and Support Vector Regression (SVR)), along with a novel Multiple Linear Regression (MLR) imputation method built upon these algorithms. The results indicate that the constructed MLR imputation method achieves an average Pearson’s correlation coefficient (PCC) of 0.9455, an average Nash–Sutcliffe Efficiency (NSE) of 0.8329, and an average Percent Bias (Pbias) of 10.5043% across different missing rates. MLR simulation results in higher NSE and lower Pbias than the other three single AI models, thus effectively improving the estimation performance. The proposed methods in this study can be applied to other river basins to improve the quality of precipitation data and support water resource management.

Ecological flow is a crucial determinant of river ecosystem well-being and aquatic ecosystem stability. Large- and medium-sized reservoirs, with flood prevention, irrigation, and power generation functions, necessitate a scientifically devised ecological flow release plan for river ecosystem conservation and water quality amelioration. This study centered on three reservoirs in the Jiaojiang River Basin of Zhejiang Province, China. Using measured outflow data, the hydrological approach was initially adopted to calculate individual reservoir ecological flows. Subsequently, the entropy weight method was employed to ascertain the most suitable ecological flow. Ecological flow grade thresholds were then established to formulate the optimal release scheme. The outcomes demonstrated that the average ecological flows of Xia’an, Lishimen, and Longxi reservoirs were 1.90 m³/s, 1.95 m³/s, and 0.42 m³/s, respectively. The multi-year average ecological flow assurance rates were 62.53%, 77.72%, and 56.94%, successively. The entropy weighted downstream optimal ecological flows were 2.10 m³/s, 2.28 m³/s, and 0.44 m³/s. During periods when the monthly ecological flow assurance rate was below 60%, the three reservoirs implemented schemes of installing ecological siphons, renovating water diversion systems, and using post-dam ecological units, respectively.

Baseflow is the part of streamflow that is mainly replenished by groundwater. The protection of the biological environment and the growth of its water resources greatly depend on the spatial and temporal evolution of baseflow. Therefore, the Baizhiao (BZA) and Shaduan (SD) catchments of the Jiaojiang River Basin (JRB) in the Zhejiang province of China were selected as study areas. The ABCD model and Eckhardt method were used to calculate baseflow and baseflow index (BFI). The temporal and spatial evolution patterns of baseflow were analyzed through statistical analysis and the Mann–Kendall test. The results showed that the ABCD model performs well in simulating overall hydrological processes on the monthly streamflow at BAZ and SD stations with NSE (Nash–Sutcliffe Efficiency) values of 0.82 and 0.83 and Pbias (Percentage Bias) values of 9.2% and 8.61%, respectively. The spatial–temporal distribution of the BFI indicates the higher baseflow contribution in upstream areas compared to downstream areas at both stations. The baseflow and BFI had significant upward trends at the BZA and SD stations in the dry season, while their trends were not uniform during the wet period. These findings are essential guidance for water resource management in the JRB regions.

In recent years, the irregular wave characteristics of ocean dynamics have often been overlooked in the study of the driving mechanism of groundwater movement in coastal aquifers. To clarify the propagation mechanisms of groundwater fluctuations driven by irregular waves in beach aquifers, we employed spectral analysis based on numerical simulations to examine the energy migration processes and evolution characteristics of wave signals at different frequencies. It elucidates the response mechanism of groundwater movement characteristics (head, velocity) to irregular waves in the sea. The energy density in the low-frequency region is enhanced compared to the incident wave and continuously increases in the direction away from the sea within the aquifer. The wavelet power corresponding to the 1/2 spectral peak frequency is significantly enhanced. The energy density in the high-frequency region is generally weaker than that of the incident waves, and the wavelet power corresponding to double spectral peak frequency is enhanced. The correlation between incident waves and groundwater fluctuations is highest near the spectral peak period. This study addresses some problems in modeling surface water–groundwater interactions under irregular wave conditions and provides a theoretical reference for investigating the impacts of extreme climate events (such as typhoon waves and low-frequency offshore oscillations generated by storm surges) on seawater intrusion into coastal groundwater systems.

Drought is one of the most widespread natural disasters globally, and its spatiotemporal distribution is profoundly influenced by the El Niño-Southern Oscillation (ENSO). As a typical humid coastal basin, the Jiaojiang River Basin in southeastern China frequently experiences hydrological extremes such as dry spells during flood seasons. This study focuses on the Jiaojiang River Basin, aiming to investigate the response mechanisms of drought evolution to ENSO in coastal regions. This study employs 10-day scale data from 1991 to 2020 to investigate the drought mechanisms driven by ENSO through a comprehensive framework that combines standardized indices with climate–drought correlation analysis. The results indicate that the Comprehensive Drought Index (CDI), integrating the advantages of the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI), effectively reflects the basin’s combined meteorological and hydrological wet-dry characteristics. A strong response relationship exists between drought indices in the Jiaojiang River Basin and ENSO events. Drought characteristics in the basin vary significantly during different ENSO phases. The findings can provide theoretical support for the construction of resilient regional water resource systems, and the research framework holds reference value for sustainable development practices in similar coastal regions globally.