The fluctuation of the tide is closely related to the production and life of people in coastal areas, and the change in the tide level will have a significant impact on the safety of infrastructure, ship travel, ecological environment, and other issues. Therefore, it is of great significance to analyze, study, and forecast the change in tide level. Aiming at the complex characteristics of nonlinearity, time-varying dynamics, and uncertainty generated by celestial bodies’ movements and influenced by geographical as well as hydrometeorological factors, this paper proposes a combined model based on variational mode decomposition (VMD) and long short-term memory neural networks (LSTM). A tidal level prediction procedure is proposed by combining the harmonic analysis method with a neural network and takes the point tide data of Luchao Port from 2021 to 2022 as the applied data. First, the VMD algorithm decomposes the tidal level data into model components. Then, the LSTM model is used to predict each component. Finally, the predicted value of each component is superposed to obtain the final prediction result. Standard evaluation indexes were used to analyze the performance of the proposed model. The model’s RMSE, MAE, MAPE, and R² were 0.0385, 0.0267, 5.8327, and 99.91%, respectively, superior to other compared models (BP, SVM, and LSTM). This study can provide a reference for tidal level prediction. These results show that the VMD-LSTM model is an effective and reliable tidal level prediction tool with considerable potential in offshore engineering and maritime management.

Traditional univariate drought indices may not be sufficient to reflect comprehensive information on drought. Therefore, this paper proposes a new composite drought index that can comprehensively characterize meteorological and hydrological drought. In this study, the new drought index was established by combining the standardized precipitation index (SPI) and the standardized baseflow index (SBI) for the Jiaojiang River Basin (JRB) using the copula function. The prediction model was established by training random forests on past data, and the driving force behind the combined drought index was explored through the LIME algorithm. The results show that the established composite drought index combines the advantages of SPI and SBI in drought forecasting. The monthly and annual droughts in the JRB showed an increasing trend from 1991 to 2020, but the temporal characteristics of the changes in each subregion were different. The accuracies of the trained random forest model for heavy drought in Baizhiao (BZA) and Shaduan (SD) stations were 83% and 88%, respectively. Furthermore, the Local Interpretable Model-Agnostic Explanations (LIME) interpretation identified the essential precipitation, baseflow, and evapotranspiration features that affect drought. This study provides reliable and valid multivariate indicators for drought monitoring and can be applied to drought prediction in other regions.

Understanding the impact of climate change and human activities on the hydrological cycle of any watershed can provide a scientific basis for regional water resource planning, flood management, and disaster mitigation. An improved three-parameter hydrological model (CM) based on monthly water balance using an exponential equation to depict the distribution of groundwater storage capacity was developed and evaluated. The model uses Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) rainfall data as input, with the Zhejiang Province as the case application, and the effects of climate change and human activities on streamflow changes were assessed by separating environmental variables in this study. The results indicate that APHRODITE data has excellent monthly accuracy, with a mean correlation coefficient (CC) of more than 0.96 and an average absolute percentage bias (Pbais) of less than 5%. The three models are relatively close in their ability to simulate high flows, but the CM simulated low flow is better than the other two models. Positive and negative Pbais phenomena occur in the CM model in each catchment, and absolute levels are regulated by 5%. Furthermore, the CM model’s average Nash efficiency coefficient (NSE) is greater than 0.9, indicating that it can correctly fulfill the water balance. The results are more consistent throughout multiple catchments in each watershed using Budyko-based and hydrological model technique to evaluate the influence of climate change and human activities on streamflow. Climate change dominated streamflow variations in 18 of the 21 catchments in Zhejiang Province, whereas human activities dominated the rest. The findings of the study will be used to influence the management, development, and usage of water resources in the watershed.

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.