[Objective] This study aims to scientifically recognize and evaluate the equilibrium of domestic, ecological, and production water use in Yangtze River Basin from the perspectives of water and soil spatial matching and differences in per capita water use. [Methods] The Gini coefficient and Lorenz asymmetry coefficient were used to analyze the disequilibrium of water use and its sources. Furthermore, the matching degree index was used to analyze the matching degree between water use, regional area, and population. [Results] The water usage per unit area in Yangtze River Basin was 92.7 mm, and the per capita water use was 1 194.0 L/d. Among these, production water use was the highest, followed by domestic water use, while ecological water replenishment was the lowest. Analysis based on water use per unit area and per capita water use showed that, overall, water use was higher in the eastern region, followed by the central region, and lower in the western region. Based on the administrative divisions, Yangtze River Basin was divided into 18 sub-regions according to provincial administrative boundaries. The Gini coefficients and Lorenz asymmetry coefficients for domestic, ecological, production, and total water use in Yangtze River Basin were calculated from the perspectives of water and soil spatial matching and differences in per capita water use. The Gini coefficients for the spatial distribution of domestic, ecological, production, and total water use in Yangtze River Basin were 0.42, 0.53, 0.51, and 0.49, respectively. The Gini coefficients for per capita water use were 0.10, 0.37, 0.25, and 0.21, respectively. Further analysis using matching degree index to evaluate the matching degree between water use, regional area, and population revealed that the matching degrees for domestic, ecological, production, and total water use with regional area were 0.83, 0.80, 0.81, and 0.81, respectively, and with population were 0.97, 0.90, 0.92, and 0.93, respectively. [Conclusion] Differences in water use are constrained by the inherent endowment of water resources, and the uneven spatial distribution of water resources directly leads to the disequilibrium in water use. The spatial distribution of domestic, ecological, production, and total water use in Yangtze River Basin exhibits disequilibrium, while per capita water use is relatively balanced, with a high degree of matching between water use, regional area, and population. Water resource management must balance “people-oriented” and “spatial equity” principles, coordinating domestic, ecological, and production water use through technological advancements and institutional innovations to reduce disparities in water use per unit area and per capita water use. The research findings can provide important support for the rational utilization of water resources and the realization of human-water harmony.

[Objective] This paper aims to reveal the hydrological evolution patterns of transboundary rivers on the western Yunnan Plateau (the Lancang River, Nu River, and Irrawaddy River) during 1956-2016, to quantify the spatial differentiation characteristics of changes in water resources in the three major river systems, and to establish a water quantity prediction method based on multi-scale periodic analysis. [Methods] Based on precipitation and runoff data of the three major river systems in Southwest China from 1956 to 2016, the Mann-Kendall trend test method was used to analyze the long-term variation trends of hydrological elements. The Morlet wavelet analysis method was applied to identify the multi-scale periodic characteristics of hydrological sequences, and the phase extrapolation method was employed to predict future water quantity variation trends. [Results] The water yield modulus of the study area reached 78.83×10⁴ m³/km²; however, all three river systems exhibited significant decreasing trends. Among them, the attenuation rate of the Irrawaddy River (-0.175×10⁸ m³/a) was significantly higher than that of the Lancang River (-0.024×10⁸ m³/a). A dominant 24-year hydrological variation period was identified at the regional scale, and the year 2016 was located at the end of the low-frequency phase of this cycle. Combined with phase extrapolation, the results indicated that future water quantity may continue to remain relatively low. The Lancang River exhibited secondary periodic oscillations of 7-12 years, which differed from the single dominant periodic pattern observed in the Nu River and the Irrawaddy River, revealing the hydrological response heterogeneity of the Lancang River caused by its specific underlying surface conditions. Precipitation showed a significant correlation with water resources (R²>0.75); however, asynchronous characteristics were observed in the Lancang River due to its specific underlying surface conditions. [Conclusion] This study systematically quantifies the spatial differentiation characteristics of hydrological evolution in the Lancang River, Nu River, and Irrawaddy River systems, establishes a water quantity prediction method based on multi-scale periodic analysis, and further reveals the secondary periodic oscillation characteristics of the Lancang River and its differences from the Nu River and Irrawaddy River, providing new scientific evidence for transboundary river water resources management in Southwest China. The results indicate that water resources of transboundary rivers on the western Yunnan Plateau may show a persistently low trend in the future, highlighting the need to strengthen transboundary water resources coordination and to formulate adaptive water resources allocation strategies.

[Objective] This study aims to address the issues of uneven spatiotemporal distribution of water resources and the difficulty in developing integrated urban-rural water supply in mountainous cities. Based on the connotation of integrated urban-rural water supply and the characteristics of its different development modes, this study innovatively constructs a coupling coordination evaluation indicator system and a zoning model for integrated urban-rural water supply. Taking the typical mountainous city of Chongqing as a case study, water supply zoning patterns with distinct regional characteristics are identified, and differentiated development schemes for urban-rural water supply are proposed. [Methods] Eight indicators were selected from three dimensions—current status of rural water supply engineering systems, natural geographical conditions, and socio-economic development. An “engineering-natural-economic” coupling coordination evaluation indicator system for integrated urban-rural water supply was constructed, and the Delphi method was used to determine the indicator weights. Considering the mutual constraints and synergistic effects among the three subsystems, a coupling zoning model for integrated urban-rural water supply was constructed. [Results] (1) The coupling coordination degree of each district and county ranged from 0.245 to 0.877. The coupling coordination degree intervals for the urban pipeline extension mode, regional pipeline interconnection mode, regional integrated block mode, and single-village upgraded point mode were [0.8, 1], [0.75, 0.80), [0.60, 0.75), and (0, 0.60), respectively. (2) All eight districts and counties under the urban pipeline extension mode were located in the main metropolitan area. These areas had relatively flat terrain and high population density, which was conducive to the construction of large-scale water supply projects and pipeline networks. Eight districts and counties under the regional pipeline interconnection mode were mostly located in the main metropolitan area, with a small number in the Three Gorges Reservoir area of northeastern Chongqing. These areas had abundant but unevenly distributed water resources. Interconnection of regional main water supply pipelines could achieve regional water resource complementarity and pipeline network connectivity. Five districts and counties under the regional integrated block mode were distributed in the main metropolitan area and the Three Gorges Reservoir area of northeastern Chongqing. In northeastern Chongqing, small reservoirs and ponds served as the main water sources, and the scale of water supply projects was small but had integration potential. Integrating surrounding small water supply projects could enhance regional water supply security. Thirteen districts and counties under the single-village upgraded point mode were mainly located in the Three Gorges Reservoir area of northeastern Chongqing and the Wuling Mountain area of southeastern Chongqing. Due to the large terrain relief in mountainous areas, water supply projects were significantly constrained by terrain. Rural areas were remote with dispersed population, making it difficult for large-scale water supply to cover them. This mode was suitable for point-based water supply targeting individual villages. [Conclusion] (1) Four development modes suitable for integrated urban-rural water supply in Chongqing City are proposed, namely the urban pipeline extension mode, regional pipeline interconnection mode, regional integrated block mode, and single-village upgraded point mode. (2) According to the zoning results, the urban pipeline extension mode relies on its high urbanization rate to achieve full water supply coverage. The regional pipeline interconnection mode addresses elevation difference issues through interconnected pipeline networks, the regional integrated block mode forms intensive water supply units by integrating small water sources, and the single-village upgraded point mode solves drinking water problems in areas with complex terrain through decentralized water supply. The research findings provide technical support for the construction of integrated urban-rural water supply in Chongqing City and are of significant importance for ensuring regional water supply security and promoting the high-quality development of the Chengdu-Chongqing Twin-City Economic Circle.

[Objective] This study aims to develop a comprehensive diagnostic method for evaluating the water balance status across water resources, ecosystems, and socio-economic systems in the context of large-scale water management projects. The focus is on the potential water source areas of the West Route of South-to-North Water Diversion Project, analyzing the interactions between natural water balances, socio-economic demands, and ecological considerations. [Methods] The research centered on quantifying and assessing the coordination and sustainability of these systems in the West Route’s water source region from 2005 to 2020. An analytical framework based on the principles of water balance was constructed, incorporating four key components: natural water supply and demand, socio-economic water needs, ecological water consumption, and the competition between ecological and socio-economic systems within the river basin. This framework was used to quantify the water balance status by evaluating water availability, ecological functioning, and socio-economic demands over the study period. Statistical methods and multidimensional analysis were applied to calculate the coupling coordination degree, which measures the extent of coordination between water resource management, ecological protection, and socio-economic development. The study relied on regional hydrological records, socio-economic data, and ecological assessments to ensure robust and reliable results. [Results] From 2005 to 2020, the water balance of the West Route’s water source areas remained relatively stable. Importantly, the region maintained a steady equilibrium in water resources, with significant improvements in the coordination between water resources, socio-economic development, and ecological systems. The coupling coordination degree among these three systems showed a clear upward trend, reflecting the growing harmony between water management, ecological conservation, and socio-economic growth. The research highlighted that the ecological system within the water source areas effectively adapted to changes in water availability, demonstrating resilience in sustaining water use. Moreover, there was a substantial positive synergy between water resource management and ecological protection, which contributed to the stability and improvement of the regional water balance. Additionally, the study showed that the competition between ecological and socio-economic water demands became more balanced, shifting toward a more integrated approach. The region’s ecological protection strategies became better aligned with water resource management policies, resulting in improved sustainability in both ecological and economic terms. [Conclusion] The findings suggest that enhanced water use efficiency, combined with adaptive ecological protection measures, has played a pivotal role in achieving these positive trends. The study’s innovative approach—integrating natural water balance, socio-economic factors, and ecological needs—provides a comprehensive framework for evaluating the sustainability of water resources in large-scale inter-basin water diversion projects. The findings demonstrate that coupling water resource management with ecological protection is not only feasible but essential for ensuring the long-term sustainability of water source areas. In conclusion, this study underscores the importance of adopting an integrated approach to water resource management, one that recognizes the interdependencies between natural, economic, and ecological systems. The research highlights that coordinated water management, paired with adaptive ecological conservation strategies, is critical to achieving sustainable development and ensuring the resilience of water source areas in large-scale water transfer projects. Furthermore, the study suggests that such integrated management models can serve as a blueprint for other regions facing similar water resource and environmental challenges, ultimately supporting the global pursuit of water sustainability.

[Objective] In recent years, excessive exploitation of groundwater resources has led to severe depletion of water resources in the Dusitu River Basin, limiting the healthy development of the local ecological environment and economy. Therefore, accurately acquiring the long-term spatiotemporal change characteristics of water storage is crucial for the sustainable utilization of water resources. [Methods] This study employed the Bayesian three-cornered hat method to integrate three GRACE Mascon products, combined with the soil water and snow water components simulated by the GLDAS model, to generate high-precision groundwater storage change results for the Dusitu River Basin. The results were validated for accuracy using measured groundwater level data from 2018 to 2021 and the water body area of Bulong Lake extracted from satellite remote sensing. The cross wavelet transform method was further introduced to analyze the synergistic effects of precipitation, temperature, and evapotranspiration factors on groundwater storage changes in the time-frequency domain. [Results] From 2003 to 2021, terrestrial water storage and groundwater storage in the Dusitu River Basin decreased significantly at rates of -6.71 mm/a and -7.88 mm/a, respectively. Spatially, the groundwater depletion trend intensified from west to east, with the declining rate increasing from -5.71 mm/a to -9.31 mm/a. After 2018, groundwater depletion accelerated, with the decline rate increasing from -7.37 mm/a to -9.52 mm/a. The trends and seasonal characteristics of measured groundwater levels were consistent with GRACE results, with an average correlation coefficient of 0.56. The area of Bulong Lake continuously decreased at a rate of approximately -2 698 m²/a, showing significant seasonal fluctuations, which was largely consistent with the trends of groundwater storage changes in the river basin. Cross wavelet analysis showed that precipitation and groundwater storage were significantly positively correlated at the 1-month scale, while temperature and evapotranspiration were significantly negatively correlated. [Conclusion] This study significantly improves the inversion accuracy of water storage changes through multi-source GRACE data fusion, clarifies the severe reality of continuous and intensifying groundwater over-exploitation in the Dusitu River Basin, and highlights regional water resources and ecological pressures. Furthermore, precipitation is the main source of groundwater recharge, while temperature and evapotranspiration exacerbate its consumption. The research findings provide reliable technical methods and data support for water resource management in the river basin.

The Three Gorges Reservoir (TGR) is located in the transitional zone between the upper and middle reaches of the mainstream Yangtze River and has strong storage regulation capacity. Since its operation, it has achieved significant comprehensive benefits and has played a prominent role in flood control, power generation, navigation, and water resources utilization in the basin. With the successive completion and operation of several giant reservoirs in the upper reaches of the Yangtze River and the in-depth implementation of the national high-quality development strategy, the operation of the TGR is facing a more complex hydrometeorological environment and higher multi-objective requirements. Meanwhile, the construction and operation of the upstream reservoir system have significantly altered the inflow and sediment regime of the TGR, increasing the complexity of its operation and regulation. This paper reviews the status and comprehensive utilization demands of water resources of the TGR, summarizes the achievements of water resources utilization optimization and operation practices over the years, and analyzes the comprehensive utilization benefits of water resources from the aspects of flood control, power generation, navigation, ecology, and water resources utilization. By integrating medium- and long-term hydrological forecasting results, promoting potential exploitation and efficiency enhancement of the TGR is an inevitable approach to further improving the comprehensive utilization benefits of water resources. Under the new requirements of adhering to the simultaneous prevention and control of droughts and floods and strengthening cross-regional allocation and regulation of water resources between wet and dry conditions, measures are proposed to further tap the potential and enhance efficiency of the TGR, including accelerating the construction of the “three lines of defense” for rainfall and flood monitoring and forecasting, strengthening research on the unified joint operation of key reservoir systems in the Yangtze River basin under extreme inflow conditions, and promoting the development of the Digital Twin Three Gorges system.

[Objective] Numerous influencing factors contribute to the imbalance between the calculated inflow and outflow discharges in the reach between the Three Gorges and Gezhouba Dams. Analyzing the significance of individual influencing factors and identifying the key drivers underlying this imbalance are of considerable importance for formulating power generation plans and conducting hydrological analysis of the Three Gorges-Gezhouba cascade hydropower stations. [Methods] Historical records of discharge and power output in the Three Gorges Reservoir Area were collected from 2018 to 2023. Based on the calculation logic of inflow and outflow discharges, 19 potential influencing factors that may affect the inflow-outflow imbalance in the reach between the two dams were selected. Grey relational analysis (GRA) and random forest (RF) model were employed to identify the key factors contributing to the inflow-outflow discharge imbalance between the Three Gorges and Gezhouba Dams. [Results] The results of GRA showed that the grey relational grade of the total power generation discharge of the Gezhouba Dam reached 0.696, ranking first. The grey correlation degree of the power generation flow of the Three Gorges Dam was 0.695, ranking second. The grey correlation degrees of the total active power of Gezhouba Dam, the total power generation flow of the Three Gorges Dam, and the total active power of the Three Gorges Dam were 0.661, 0.651, and 0.636, respectively, ranking third, fourth, and fifth. For RF model, two methods—rank assignment summation and normalization summation—were adopted to integrate the two indicators, namely %IncMSE (percentage increase in mean squared error) and IncNodePurity (increase in node purity). The results indicated that in both methods, the total power generation discharge of the Gezhouba Dam and the storage-release discharge of the Gezhouba Dam ranked first and second, respectively, in terms of importance. Specifically, the normalization summation method not only reflected the importance ranking of different influencing factors, but also demonstrated that the relative importance of each factor through specific indicator values. Among these factors, the total power generation discharge of the Gezhouba Dam scored the highest (1.25), followed by its storage-release discharge (1.22). In contrast, the total power generation discharge of the Three Gorges Hydropower Station, which ranked third, had a significantly lower score (0.61) than the storage-release discharge of the Gezhouba Dam, which ranked second. [Conclusion] The total power generation discharge of Gezhouba Dam is the key influencing factor causing the inflow-outflow discharge imbalance. This is mainly attributed to the following reasons: 1) the Gezhouba Dam launched its capacity expansion and renovation project in 2013, with the total installed capacity of its 19 generating units increasing by 475 000 kilowatts. However, the NHQ curve adopted for calculating the power generation discharge of the units still remains the original factory curve without any updates, which leads to calculation errors in power generation discharge. 2) The head loss of the Gezhouba Dam is derived from the calculation based on the inflow discharge, instead of being accurately determined for each individual unit, and this calculation method will also induce certain errors. 3) Due to the complex flow conditions in front of the Gezhouba Dam, there are often differences in water head between the left and right banks. Nevertheless, water-head data from a single monitoring station are applied uniformly in power generation discharge calculations, which may further contribute to calculation errors.

[Objective] Developing new quality productive forces of water conservancy is an inevitable direction for promoting high-quality development of the water conservancy sector at present, and how to cultivate such forces according to local conditions has become a key issue faced by both the theoretical community and practical departments. Although related studies have continued to expand, there remain shortcomings in the water conservancy field, including insufficient systematic attention to differences in water resource endowment and an inadequate understanding of the formation mechanisms of new quality productive forces of water conservancy. Therefore, it is urgently necessary to carry out normative research focusing on the theoretical logic and practical pathways of development tailored to local conditions. [Methods] This study was based on resource endowment theory, comparative advantage theory, and new structural economics, and constructed an analytical framework for developing new quality productive forces of water conservancy according to local conditions. Starting from water factor endowments and functional attributes, the key roles of innovative allocation of water-related factors, upgrading of water-related industries, and the water-related science and technology talent system were systematically examined in promoting the development of new quality productive forces of water conservancy. Furthermore, by integrating regional water resource characteristics, the study identified five core dimensions—water science and technology, resource allocation, ecological value, industrial upgrading, and disaster prevention and control—and established a system of regionally differentiated development pathways. [Results] The results showed that developing new quality productive forces of water conservancy according to local conditions was a dynamic evolutionary process that started from water factor endowments and regional differences, progressed through the formation of comparative advantages and coordinated regional division of labor, and ultimately led to a spiral improvement of comprehensive capabilities. This development relied on three key driving forces: innovative allocation of water-related factors, which promoted the overall optimization of water engineering systems, water ecological patterns, and water resource utilization structures; in-depth development and transformational upgrading of water-related industries expanded the water economic value chain and strengthened the industrial foundation of the water conservancy modernization system; and improvements in the water-related scientific and technological innovation and talent cultivation system provided sustained momentum for the modernization of water conservancy. The study further identified five major manifestations of new quality productive forces of water conservancy from the perspective of the multiple values of water factors: water science and technology reflected innovation in governance capacity, resource allocation reflected improvements in water resource efficiency, ecological value reflected ecosystem improvement and enhanced services, industrial upgrading reflected industrial structure reshaping and value chain extension, and disaster prevention and control reflected enhanced regional water security resilience. These dimensions were interrelated and jointly constituted a diversified structural system of new quality productive forces of water conservancy. [Conclusion] Based on the characteristics of water factor endowments in different regions, this study proposes five representative types of differentiated development pathways. The northwestern arid regions are suited to adopt a “technology-based water compensation” pathway, improving water resource utilization efficiency through technological innovation. North China is suited to follow an “institutional adjustment” pathway, alleviating the contradiction between water supply and demand through institutional provision. Southwest China is suited to adopt an “ecological transformation” pathway, converting rich water ecological advantages into development momentum. The southeastern coastal regions are suitable for an “integrated water economy” pathway, strengthening the linkage between the water economy and regional industries. Typical high-risk regions need to adopt a “resilience enhancement” pathway, reinforcing flood control, disaster reduction, and comprehensive risk governance. These pathways reflect the heterogeneity of the formation mechanisms of new quality productive forces of water conservancy across regions and demonstrate the central role of the principle of adapting measures to local conditions in the modernization of water conservancy. Overall, developing new quality productive forces of water conservancy according to local conditions must be based on differences in water factor endowments and regional functions, forming an advantage-oriented and endogenous driving system, and achieving coordinated improvement in water science and technology, resource allocation, ecological value, industrial development, and disaster resilience. The results and pathway system of this study provide important theoretical foundations and practical guidance for regions to construct differentiated water conservancy development models.

[Objective] This study aims to analyze regional differences in agricultural and industrial water use efficiency in the Yangtze River Basin and their key influencing factors, reveal the spatial differentiation patterns of water use efficiency within the river basin, and provide scientific guidance for formulating differentiated and precise water resource management policies. [Methods] Three provinces (municipalities) from the upper, middle, and lower reaches of the Yangtze River were selected as sample regions. Authoritative data from sources such as the China Water Resources Bulletin, the Water Resources Bulletin of the Yangtze River Basin and Southwest Rivers, and the Statistical Bulletin on National Economic and Social Development from 2014 to 2023 were collected. Core water use efficiency indicators included: effective utilization coefficient of farmland irrigation water, actual irrigation water use per mu of farmland, and water use per 10 000 yuan of industrial added value. Using methods such as descriptive statistics and comparative analysis, the temporal changes and spatial differences in water use efficiency for the entire river basin and between regions were systematically evaluated. Additionally, combined with data on topography, per capita GDP, precipitation, and industrial structure of the sample provinces (municipalities), key factors affecting water use efficiency were qualitatively analyzed and quantitatively identified. [Results] (1) Temporal changes showed that in the past decade, both agricultural and industrial water use efficiency in the Yangtze River Basin gradually improved, but both indicators remained consistently below the national average level, indicating that the overall water-saving potential of the river basin still needed to be tapped. (2) Spatial differences were significant. Agricultural water use efficiency (characterized by the effective utilization coefficient of irrigation water) followed the order of downstream area > midstream area > upstream area. Per capita GDP and topography were key factors affecting coefficient changes. In general, regions with higher economic development level and flatter terrain exhibited higher agricultural water use efficiency. Industrial water use efficiency (characterized by water use per 10 000 yuan of industrial added value) followed the order of upstream area > midstream area > downstream area, with the rationality of industrial structure being its key influencing factor. The downstream area, due to the concentration of high water-consuming industries and a relatively large proportion of water use for direct-current thermal (nuclear) power, had relatively low industrial water use efficiency. (3) Actual irrigation water use per mu of farmland was affected by multiple factors such as cropping structure, climate variability, and irrigation methods. This resulted in poor cross-regional comparability, making it unsuitable as a reliable indicator for evaluating spatial differences in agricultural water use efficiency. [Conclusion] Economic development level, irrigation infrastructure conditions, and the rationality of industrial structure are key factors affecting agricultural and industrial water use efficiency in the Yangtze River Basin. In the upstream area, complex terrain, outdated irrigation facilities, and insufficient financial and technical support lead to significant irrigation water losses during water conveyance and use, resulting in relatively low agricultural water use efficiency. In the downstream area, although water-saving management measures are relatively well developed, the large proportion of high water-consuming industries in the industrial structure results in relatively low industrial water use efficiency. Based on these findings, to improve the overall water use efficiency of the river basin, differentiated and precise zonal management strategies should be implemented. In the upstream area, priority should be given to modernizing irrigation infrastructure and promoting advanced technologies. In the downstream area, efforts should focus on strengthening industrial structure optimization and formulating stricter water-saving standards and incentive-constraint mechanisms for high water-consuming industries. Future research could focus on smaller spatial scales and enhance the quantitative analysis of influencing factors to support the precise implementation of water-saving measures.

[Objective] To improve the accuracy of groundwater level time series prediction and explore the application effects of 17 decomposition techniques—EMD, EEMD, CEEMD, ICEEMD, LMD, RLMD, ITD, ESMD, WT, WPT, EWT, VMD, SSA, TVF-EMD, FDM, SGMD, and SVMD—in the decomposition of groundwater level time series data, a love evolution algorithm (LEA) - fast learning network (FLN) prediction model based on these 17 decomposition techniques is proposed. [Methods] Firstly, 17 decomposition techniques including EMD were used to decompose the groundwater level time series data, and several decomposition components were obtained. Secondly, based on the training set of each decomposition component, a fitness function was constructed, and LEA was used to optimize the fitness function to obtain the optimal FLN input layer weight and hidden layer threshold for FLN. Seventeen models, including EMD-LEV-FLN, were established to predict and reconstruct each decomposition component. Finally, the daily water level time series prediction of the Caoba groundwater monitoring well in Yunnan Province from 2019 to 2023 was used as an example to verify each model. [Results] (1) WPT-LEV-FLN, EWT-LEV-FLN, FDM-LEV-FLN, TVF-EMD-LEV-FLN models achieved the highest prediction accuracy, with average absolute percentage error (MAPE), average absolute error (MAE), and root mean square error (RMSE) ranging 0.000%-0.001%, 0.002-0.020 m, and 0.002-0.032 m, respectively. The determination coefficients (R²) were all 1.000 0. The SSA-LEV-FLN, WT-LEV-FLN, and VMD-LEV-FLN models came in second place, with predicted MAPE, MAE, RMSE, and R² ranging 0.003%-0.007%, 0.041-0.087 m, 0.063-0.131 m, and 0.999 5-0.999 9, respectively. Other models had relatively poor prediction accuracy, with predicted MAPE, MAE, RMSE, and R² ranging 0.017%-0.033%, 0.221-0.417 m, 0.385-0.705 m, and 0.985 3-0.995 6, respectively. Among them, WPT-LEV-FLN model had high prediction accuracy and small computational scale, demonstrating the greatest practical value and significance. (2) WPT, EWT, FDM, and TVF-EMD showed the best decomposition performance, among which WPT not only had good decomposition performance, but also produced fewer decomposition components, making it the most advantageous. SSA, WT, and VMD showed relatively good decomposition performance, and increasing the number of decomposition components could further improve the decomposition effectiveness. The other models performed relatively poorly, among which SGMD and SVMD had the least decomposition components and the greatest potential. [Conclusion] This study compares the application performance of 17 current mainstream time series decomposition techniques for processing groundwater level time series decomposition and proposes 17 prediction models, providing reference and guidance for the selection of time series decomposition methods and research on groundwater time series prediction.

[Objective] This study aims to analyze the changes in water resources in the middle and lower reaches of the Yangtze River and to investigate the impact of El Niño events on regional floods. [Methods] GRACE gravity satellite data from 2003 to 2022, released by various research institutions, were used to derive the Terrestrial Water Storage Anomaly (TWSA) for the study area. Based on correlation coefficients and cross-correlation analysis, the CSR MASCON TWSA data series exhibiting strong correlations with the indices of both Eastern Pacific (EP) and Central Pacific (CP) El Niño events was selected. Wavelet analysis, Empirical Orthogonal Function (EOF) analysis, and the Flood Potential Index (FPI) were employed to investigate the influence of the two types of El Niño events on regional TWSA and to analyze their relationship with flood risk in the study area. [Results] The results were as follows: (1) the highest correlation between TWSA and the EP El Niño event was found at a time lag of 6 months, with a correlation coefficient reaching 0.630. TWSA peaks showed a positive response to EP El Niño events, while the response to CP El Niño events was unstable. (2) Cross wavelet transform revealed common resonance periods between TWSA and both types of El Niño events, and the impact of the EP El Niño event on water resource changes in the middle and lower reaches of the Yangtze River was found to be more significant. The EOF analysis showed that the southern part of the study area was susceptible to the influence of both El Niño types. (3) The spatial distribution of the grid-based Flood Potential Index showed a higher flood risk in the southern part of the study area following the occurrence of both El Niño types. The flood risk corresponding to EP El Niño events was greater, with high-risk areas concentrated at the junction of the Dongting Lake and Poyang Lake sub-basins. In contrast, the flood risk distribution corresponding to CP El Niño events was more dispersed. [Conclusion] The results of this study, based on wavelet analysis, EOF analysis, and the Flood Potential Index, show that flood risk in the middle and lower reaches of the Yangtze River is closely related to El Niño events. These findings contribute to the prediction and prevention of floods in the region.

[Objective] The simplified structures of traditional hydrological models often limit their adaptability under complex hydroclimatic and anthropogenic conditions. Meanwhile, data-driven models such as deep learning frameworks typically lack explicit physical interpretability. To address these challenges, this study compares the performance of three representative runoff prediction methods: two physically based models—the Xinanjiang (XAJ) model and Soil & Water Assessment Tool (SWAT) model—and one data-driven model, the Long Short-Term Memory (LSTM) model. The Xiangjiang River Basin, a major tributary of the Yangtze River in southern China, is selected as the study area. This study aims to evaluate model performance and adaptability across multiple temporal scales. [Methods] The dataset included daily runoff records from 1971 to 2020 at the Xiangtan hydrological station, along with concurrent meteorological observations from 32 meteorological stations. Land use data with a spatial resolution of 1 km × 1 km were obtained from the Chinese Academy of Sciences, and soil property data were derived from the Harmonized World Soil Database (HWSD). The employed XAJ model estimated evaporation from three soil layers, routed surface runoff using the unit hydrograph method, and modeled interflow and groundwater components through the linear reservoir approach. The SWAT model divided the river basin into 13 subbasins and 192 hydrological response units (HRU) using high-resolution spatial inputs. For both models, the SUFI-II algorithm was employed for parameter calibration based on the Nash-Sutcliffe Efficiency (NSE) coefficient and the total water balance error. The LSTM model was trained using climate indices as inputs under different strategies (areal averages and multi-station series). The input indices included precipitation, temperature, and relative humidity. To evaluate the effects of temporal dependencies, lag times from 0 to 5 days were tested. All three models were calibrated using data from 1971 to 2010 (with the first two years as a warm-up period) and were validated over 2011-2020. [Results] (1) For daily-scale prediction, the LSTM model achieved the highest accuracy, with NSE values reaching 0.99 during calibration and 0.87 during validation when using multi-station meteorological inputs. Incorporating spatially distributed meteorological inputs significantly improved LSTM performance compared to using areal averages, highlighting the importance of spatial heterogeneity in data-driven hydrological forecasting. The XAJ model performed robustly (NSE>0.76), especially during flood seasons, but tended to overestimate dry-season flows and underestimate flood peaks. The SWAT model (NSE≈0.6) reproduced the overall hydrograph patterns but showed systematic biases similar to those of the XAJ model. (2) At the monthly scale, the performance of the SWAT model improved significantly (NSE=0.92 during calibration, 0.83 during validation), while LSTM accuracy declined (NSE = 0.86 during validation). The reduced training sample size (480 months) likely caused overfitting in the LSTM model and limited its generalization ability. Incorporating temperature and humidity as input features enhanced the stability of the LSTM model, indicating that variables related to evapotranspiration became more influential at coarser temporal resolutions. (3) Both hydrological models showed distinct seasonal bias patterns: overestimation during dry seasons and underestimation during wet seasons. The main reason was the fixed parameterization that failed to represent temporal variability in infiltration and storage processes. In contrast, the flexibility of the LSTM model enabled better adaptation, though at the cost of physical interpretability. The observed discrepancies also reflected the impact of nine cascade hydropower projects along the mainstream of the Xiangjiang River, which regulated flow seasonality but were not explicitly modeled in the hydrological frameworks used in this study. [Conclusion] In summary, this study systematically evaluates the performance of the XAJ, SWAT, and LSTM models for runoff forecasting in the Xiangjiang River Basin at daily and monthly scales. The results demonstrate that the LSTM model achieves the highest forecasting accuracy and computational efficiency, particularly at the daily scale. In contrast, the XAJ model is more reliable during flood seasons, and the SWAT model is more suitable for representing the long-term spatiotemporal variability of hydrological processes. Although the data-driven LSTM model lacks explicit physical mechanisms, it offers substantial advantages in adaptability and predictive precision, whereas physically based models maintain interpretability and stability under limited data conditions. The innovation of this study lies in bridging process-based and deep learning methods under unified experimental conditions, emphasizing the potential of hybrid modeling frameworks that integrate physical constraints with deep neural architectures to enhance both accuracy and interpretability in future runoff forecasting applications.

[Objective] This study aims to develop a daily-scale precipitation fusion product (2001-2023) with higher spatiotemporal accuracy covering the Yangtze River Basin by utilizing multi-source data and machine learning techniques, to address the poor quality of existing single or fusion products and to provide reliable data support for related research and applications in this region. [Methods] Multiple types of fundamental geographic data and in-situ measured precipitation data were collected and processed. Based on the aforementioned data, eight machine learning models—RF, CatBoost, KNN, Lasso, DTREE, XGBoost, HGBR, and ETREE—were selected for preliminary training, and their comprehensive capabilities were quantitatively evaluated. Subsequently, nine different ensemble model combinations were constructed based on the single models, and through quantitative evaluation, the seasonal ensemble model ELM4-S with the best overall performance was identified to generate the final daily precipitation fusion product for the Yangtze River Basin at a 0.1° resolution. [Results] (1) Based on multiple evaluation metrics, among the four original precipitation products (ERA5, ERA5-Land, GPM, and CMORPH) in the Yangtze River Basin, GPM exhibited the best overall performance. In terms of the probability of detection (POD), the ERA5 series demonstrated particularly outstanding performance, reaching 0.96. (2) A comparison of the performance of the eight machine learning models (RF, CatBoost, KNN, Lasso, DTREE, XGBoost, HGBR, and ETREE) indicated that RF exhibited the best overall performance. After training, all machine learning models achieved satisfactory results and outperformed the original precipitation products in terms of correlation (R), root mean square error (RMSE), and mean relative bias (MRB). (3) Among the nine ensemble models constructed from combinations of different machine learning models, ELM4-S demonstrated the best overall performance. The fusion precipitation product obtained by ELM4-S was superior to the original precipitation products, incorporating the advantages of different original products. It was numerically reasonable and could reflect the detailed characteristics of precipitation variation with topography in its spatial distribution. [Conclusion] The precipitation fusion product generated based on the ELM4-S model is more accurate than the four original gridded precipitation products adopted. This product not only integrates the advantages of each original dataset but also finely captures the spatial distribution characteristics of precipitation variation with topography, exhibiting outstanding detail. This study successfully develops a high-precision daily precipitation fusion product for the Yangtze River Basin from 2001 to 2023 using an ensemble machine learning approach. This product effectively balances POD and false alarm rate (FAR). It outperforms the original data and single-model results in overall performance and captures more reasonable spatial details of precipitation. It can serve as a reliable data product to widely support various production applications and scientific research within the basin.

[Objective] To address the challenges faced by Horizontal Acoustic Doppler Current Profilers (H-ADCP) in online discharge monitoring applications—specifically, the difficulty in selecting index velocity (feature cells), the insufficient non-linear expressiveness of traditional calibration models, and the poor generalization ability and high computational complexity of existing machine learning models under complex hydrodynamic conditions such as tides and engineering regulations—this paper aims to develop a new H-ADCP online discharge monitoring model that can automatically optimize velocity features, integrate the advantages of multiple algorithms, and improve model accuracy. This model is designed to address the complex non-linear mapping problem between high-dimensional velocity data and cross-sectional discharge, thereby enhancing the accuracy, stability, and automation of discharge monitoring. [Methods] A Feature Adaptive Optimization (FAO) model for H-ADCP online discharge monitoring was developed. The technical framework of this model comprised three core components: (1) feature dimensionality reduction: Principal Component Analysis (PCA) was applied to conduct initial dimensionality reduction on the high-dimensional velocity data from up to 128 cells generated by the H-ADCP, reducing subsequent computational complexity while preserving the main velocity distribution characteristics. (2) Multi-model parallel mapping: five machine learning models—Backpropagation (BP) Neural Network, Elman Neural Network, Radial Basis Function (RBF) Network, Generalized Regression Neural Network (GRNN), and Support Vector Machine (SVM)—were constructed in parallel to establish the non-linear mapping relationship between the dimension-reduced feature velocities and the measured cross-sectional discharge. (3) Global optimization and adaptive selection: the Particle Swarm Optimization (PSO) algorithm was utilized as a global optimization engine, with the Root Mean Square Error (RMSE) as the fitness function, to search within the feature subspace and model space through iterative optimization and adaptively determine the optimal combination of velocity cells, the best machine learning model, and its corresponding parameters. To validate the model’s performance, the Luohu Hydrological Station, which is affected by both tides and backwater effects from confluence and has a complex hydrological regime, was selected as the study area. The model was calibrated and verified using measured H-ADCP velocity data and comparative discharge data from a moving-boat ADCP for the years 2019 and 2023. [Results] (1) The FAO model demonstrated superior performance: during the 2019 model verification period, the discharge predictions of the FAO model showed a high degree of agreement with the measured values, with a RMSE of 6.06 m³/s and a Coefficient of Determination (R²) reaching 0.93. This was significantly better than the traditional linear regression model and any single machine learning model. In simulating extreme discharges such as flood peaks, the FAO model also demonstrated a greater ability to capture them, with an annual maximum discharge error of 1.56%. (2) The feature optimization was effective: the model successfully and automatically selected an optimal combination of 11 feature cells ({5,9,12,15,17,19,21,24,26,28,35}) from 40 velocity measurement cells, eliminating invalid data affected by riverbanks and blind zones. The distribution pattern of the selected cells was highly consistent with hydraulic characteristics, demonstrating the physical interpretability of the model’s feature selection. (3) The model showed strong stability: when validated with data from the entire year of 2023, the FAO model performed stably, with an RMSE of 6.02 m³/s and an R² of 0.91, and effectively fitted the entire annual discharge process, especially for maximum and minimum values. [Conclusion] The proposed FAO model, by organically integrating PCA, multiple machine learning algorithms, and the PSO optimization algorithm, successfully addresses the key technical challenges in H-ADCP online discharge monitoring. The model exhibits powerful self-learning and self-adaptive capabilities, enabling it to automatically find the optimal velocity features and computational model based on data samples, while ensuring computational accuracy and significantly reducing data processing complexity. The application case under complex hydrological conditions demonstrates that the FAO model has high accuracy, good stability, and strong adaptability, providing an efficient and intelligent solution for H-ADCP online discharge monitoring.

[Objective] Improving the prediction accuracy of medium- and long-term hydrological forecast is of great significance for water resources scheduling, flood control and drought relief, and agricultural production. This study aims to select reliable, efficient, and practical hybrid machine learning models to improve forecasting performance for highly irregular, complex nonlinear, and multi-scale variable medium- and long-term hydrological forecasts, providing new approaches for enhancing hydrological forecast accuracy in changing environments. [Methods] To improve the accuracy of hydrological forecasts, based on the measured monthly runoff series at Wanxian Station in the Three Gorges Reservoir area, the mutual information method was used to screen forecasting factors. Then, Long Short-Term Memory (LSTM) models optimized by the Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), and Sparrow Search Algorithm (SSA) were established. Combined with Time-Varying Filtered Empirical Mode Decomposition (TVF-EMD), Complementary Ensemble Empirical Mode Decomposition (CEEMDAN), and Variational Mode Decomposition (VMD), multiple hybrid prediction models were established. Their prediction performance was evaluated using five indicators: mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), mean absolute percentage error (MAPE), and correlation coefficient (R). [Results] The forecast factor scheme selected by the mutual information method provided optimal model input, with a lag of 15 months achieving the maximum mutual information value and minimum MASE, representing the best input configuration. Among the three single machine learning models, LSTM and SVM outperformed BP, with LSTM and SVM showing similar performance. LSTM was preferred due to its sensitivity to temporal sequences, enabling better handling of nonlinear runoff prediction, and was thus used in coupling with different methods for runoff forecasting. The hybrid models following the “decompose-reconstruct” strategy outperformed single LSTM models: the VMD-LSTM model improved the NSE of the test set by 0.12 compared with the single LSTM model, exceeding CEEMDAN-LSTM and TVF-EMD-LSTM. Further integration with robust optimization algorithms enhanced accuracy: the VMD-SSA-LSTM model outperformed VMD-LSTM, VMD-GOA-LSTM, and VMD-WOA-LSTM, showing superior adaptability, generalization, and overall predictive performance. [Conclusions] Machine learning models provide effective runoff forecasting methods for regions with limited hydrological and meteorological data. The approach of combining forecasting factor screening, data preprocessing, and integrating robust optimization algorithms with the model can further improve the accuracy of a single hydrological forecasting model. The established VMD-SSA-LSTM model achieved test period performance of MAE=32.65, RMSE=43.44, NSE=0.95, MAPE=12.9%, and R=0.98, representing the highest accuracy among compared models. This model meets practical production and daily life requirements and can provide a reference for water resource management and industrial and agricultural production in the studied basin.

[Objective] Against the background of rapid urbanization, changes in the urban underlying surface constitute a significant factor influencing runoff processes, yet their mechanisms remain inadequately studied. [Methods] Taking Qingshan District of Wuhan City as a representative study area, this paper used remote sensing technology, GIS analysis, and a BP neural network model to quantitatively assess urban underlying surface changes during the typical study period and analyze its impact on the runoff coefficient. [Results] (1) Under urban development, land use in the study area from 2002 to 2017 shifted overall from permeable to impermeable surfaces. Vegetation, rooftops, and other land-use types fluctuated, whereas water bodies shrank year by year. Construction of the sponge city demonstration zone in 2015 slowed this trend. (2) The runoff coefficient was jointly affected by underlying surface changes and rainfall. However, urban rainfall changed little over short timescales, the impervious surface ratio was the dominant factor. As the area ratio of high-runoff land use (e.g., hardened ground) increased and that of low-runoff land use (e.g., vegetation, green space) decreased, the runoff coefficient rose yearly—from 0.399 in 2009 to 0.535 in 2017—showing that land-use change directly altered the runoff coefficient to some extent. (3) After sponge city interventions, the annual runoff coefficient showed a decreasing trend; in 2017 it was 0.535, 0.051 lower than in 2014. [Conclusions] Sponge city construction reduces the runoff coefficient by expanding highly permeable surfaces and adding storage volume, thereby mitigating the adverse impacts of urban development on stormwater regulation capacity. The study offers scientific guidance for urban planning and flood-control drainage system design, and technical support for urban hydrological cycles and water-resource management.

[Objective] In response to the operational challenge caused by high penetration of wind and solar power in modern power systems, this study aims to propose a bi-level optimized scheduling model for a multi-energy complementary power generation system incorporating pumped storage. The model seeks to enhance renewable energy utilization, optimize system economic performance, and improve system stability. The novelty lies in the integration of a bi-level optimization framework with a deep peak shaving strategy, while introducing CO₂ emission intensity and thermal power output coefficient as evaluation indicators for multi-objective coordination of economy, environmental performance, and stability. [Methods] The upper-level model optimized the joint dispatch of wind, solar, hydro, and pumped storage with objectives of maximizing wind and solar output, minimizing net load fluctuation, and minimizing curtailed electricity. The lower-level model optimized the economic performance of the system, aiming at minimizing thermal power operational costs, pumped storage costs, and curtailed electricity penalties. Constraints included wind and solar output limits, hydro and pumped storage capacity limits, thermal unit ramping capabilities, and power balance requirements. The CPLEX solver combined with the YALMIP toolbox was employed to solve the high-dimensional nonlinear mixed-integer programming problem. CO₂ emission intensity and thermal power output fluctuation coefficient were adopted as additional evaluation metrics to quantify environmental performance and system stability. [Results] Simulation results indicated that integrating pumped storage reduced total cost by 46 000 CNY (1.02%) and CO₂ emission intensity by 6.4% in the summer scenario, while the thermal power output fluctuation coefficient decreased from 33.34% to 7.88%. In winter, thermal output stability improved to 7.67%. Increasing wind-solar penetration from 31.25% to 47.62% lowered system costs by 39.5% and reduced CO₂ emission intensity by 58.3%. Enhancing deep peak shaving from 50% to 70% reduced total cost by 19.2% and decreased thermal power output fluctuation coefficient by 44.2%. [Conclusions] The introduction of pumped storage power station significantly enhances system flexibility, increasing renewable energy utilization by over 12% and reducing thermal unit peak regulation pressure by 50%. The bi-level optimization model ensures low-cost operation while reducing CO₂ emission intensity by more than 0.1 kg/kWh and maintaining thermal power output fluctuation coefficient below 8%. A combination of high wind-solar penetration (>40%) and deep peak shaving (70%) achieves optimal comprehensive benefits, providing theoretical support for scheduling of high renewable energy penetration power systems. This study provides an innovative methodology for the design and optimization of wind-solar-hydro-thermal-pumped storage multi-energy systems, and the findings can be generalized to other clean energy bases.

[Objective] This study aims to conduct a comprehensive evaluation of the ecological benefits of the South-to-North Water Diversion Project (SNWDP) by systematically quantifying the ecological benefits in the water-receiving areas during the first phase of the Middle Route Project. [Methods] The water receiving area was divided according to administrative units and assessed using statistical and remote sensing data. Taking 2014 as the base year and 2018, 2020, and 2023 as evaluation years, we evaluated the ecological benefits brought by project-supplied water in Beijing, Tianjin, 11 counties (or cities) of Henan, and 6 counties (or cities) of Hebei. Ecological benefit index systems were established for forest land, urban green space, wetlands, water bodies, and groundwater ecosystems by integrating the function value method and the equivalent factor method. For forest land, urban green space, and groundwater ecosystems, multiple ecosystem service functions were quantitatively analyzed. The market value method, replacement cost method, and other valuation methods were used to estimate the unit prices of each function and calculate their total service value. For wetlands and water body ecosystems, ecological benefits were calculated using the equivalent factor method based on regional characteristics. A spatiotemporal precipitation adjustment factor was introduced to dynamically adjust the factor values in the basic equivalent factor table, thereby determining the value of one standard unit of ecosystem service equivalent factor. [Results] Cumulative ecological benefits generated by the water supply from the first phase of the Middle Route Project amounted to 44.859, 18.328, and 37.102 billion yuan in each evaluation period, respectively. Wetlands and water bodies accounted for the largest proportions, at 64.90%, 58.98%, and 46.98%, respectively. From 2015 to 2018, new ecological benefits from water bodies and wetlands reached 24.724 and 4.391 billion yuan, respectively; for 2019-2020, they were 9.100 and 1.709 billion yuan; and from 2021 to 2023, new ecological benefits from wetlands and water bodies were 11.079 and 6.352 billion yuan, respectively. The annual average new ecological benefits for each period were 11.215, 9.164, and 12.367 billion yuan, indicating that the project’s water supply generated approximately 10 billion yuan of ecological benefits per year in the water receiving areas. In addition, the ecological benefit value per cubic meter of water varied across provinces and cities. In Beijing, the values were 1.64, 1.38, and 3.01 yuan; in Tianjin, 3.34, 2.19, and 0.52 yuan; in Henan’s 11 counties, 8.16, 5.06, and 3.79 yuan; and in Hebei’s 6 counties, 6.12, 2.69, and 4.07 yuan, respectively. The benefit value ratios for Beijing∶Tianjin∶Henan∶Hebei in each evaluation period were 1∶2.10∶4.99∶3.74, 1∶1.59∶3.66∶1.95, and 1∶0.17∶1.26∶1.35, respectively. [Conclusion] This study provides a case reference for ecological benefit evaluation the follow-up projects of the SNWDP and other inter-basin water diversion projects. It provides technical support for the scheduling and utilization of ecological benefits of the Middle Route Project, and further provides a calculation basis for promoting the establishment of horizontal ecological compensation standards between the water receiving and source areas.

[Objective] This study aims to reveal the influence mechanisms of hydrothermal conditions on the spatiotemporal variability of hyporheic exchange and to develop more reliable estimation methods. We acknowledge the limitations of single methods and innovatively propose an estimation framework for hyporheic exchange that integrates hydraulic methods, environmental tracer methods, and numerical simulation technologies. The proposed method is expected to address the insufficient accuracy and scale mismatch in existing estimation methods and to enhance the capacity to quantify highly variable hyporheic exchange fluxes. [Methods] First, based on years of practical experiences, and combined with a systematic review and critical analysis of existing literature, we deeply analyze the intrinsic driving mechanisms of the spatiotemporal variability of hyporheic exchange from two core perspectives: hydraulics and thermodynamics. Second, we propose an integrated multi-method estimation framework to improve the accuracy and robustness of the estimation results. [Results] The mechanisms by which hydrothermal conditions drive the spatiotemporal variability of hyporheic exchange are summarized as follows.(1) Hydrological rhythm: the dynamic variations in river water level and discharge alter the hydraulic head difference between river water and groundwater, serving as the primary driver of the temporal changes in the rate and direction of hyporheic exchange.(2) Topography, geomorphology, and bed heterogeneity: local topographic features of rivers and lakes (such as sand bars, pools, and point bars) and the spatial heterogeneity of riverbed sediments shape the spatial distribution pattern of hydraulic head differences, which is the fundamental cause of significant spatial variations in hyporheic exchange.(3) Temperature variation: strong daily temperature differences can generate significant thermal gradients within riverbed sediments, inducing rapid flows and shaping the diurnal variation patterns of hyporheic exchange.(4) Seasonal freezing and thawing processes substantially alter the spatial structural characteristics of riverbed permeability, profoundly affecting both the intensity and spatial extent of hyporheic exchange at seasonal and spatial scales. These driving factors are often in a state of nonstationary variations and exhibit complex couplings. Collectively, their combined effects make the spatiotemporal variation patterns of the hyporheic exchange difficult to be accurately captured or predicted by simple methods. [Conclusion] This study systematically elucidates the mechanisms by which hydrothermal conditions jointly influence the complex spatiotemporal variations of hyporheic exchange through hydraulic and thermodynamic processes. It deepens the understanding of surface water-groundwater interactions, providing a theoretical basis and practical guidance for developing more accurate watershed hydrological models, assessing the health of river ecosystems, and formulating science-based ecological restoration strategies for rivers and lakes.

[Objective] To address the low accuracy of monthly runoff point prediction and the difficulty in describing the uncertainty of point prediction results, this study proposes a monthly runoff point prediction model and an interval prediction model based on the Crested Porcupine Optimizer (CPO), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM) network, and Nonparametric Kernel Density Estimation (NKDE). [Methods] First, a hybrid point prediction model (CPO-CNN-BiLSTM) was developed. CPO was used to optimize key model parameters such as the number of hidden layer nodes, initial learning rate, and regularization coefficient. Monthly runoff data and its influencing factors were input to the model to obtain point prediction results. Next, the point forecasts were sorted using a range segmentation method and divided into low, medium, and high flow segments. The relative error for each predicted value within these segments was calculated. The NKDE method, with window width optimized by CPO, was employed to estimate the error probability distribution function for each segment. Cubic spline interpolation was then applied to fit the probability distribution functions of the three segments and derive segment-specific quantiles, forming a monthly runoff interval prediction model (CPO-CNN-BiLSTM-NKDE) based on NKDE method and the CPO-CNN-BiLSTM model. Finally, the runoff point forecasts were combined with the corresponding quantiles of their flow segments to generate monthly runoff interval predictions. Case studies compared the proposed CPO-CNN-BiLSTM point prediction model with traditional models including Least Squares Support Vector Machine (LSSVM), Kernel Extreme Learning Machine (KELM), LSTM, and BiLSTM, using RMSE, MRE, and MAPE as evaluation metrics. [Results] The CPO-CNN-BiLSTM model’s prediction accuracy was significantly better than the other models, especially during flood and dry seasons. Compared with the best-performing among the other four models in terms of RMSE, MRE, and MAPE, the values decreased by 43.71%, 38.56%, and 24.38%, respectively. This indicated a superior ability to accurately predict peak and valley runoff values. Additionally, deep learning models (LSTM, BiLSTM, CNN-BiLSTM) outperformed machine learning models (LSSVM, KELM), with the BiLSTM model surpassing LSTM, and the CNN-BiLSTM hybrid outperforming both. The proposed CPO-CNN-BiLSTM-NKDE interval prediction model was compared with other interval prediction models at confidence levels of 95%, 90%, and 85%, and it exhibited the highest Prediction Interval Coverage Probability (PICP)and the lowest Prediction Interval Normalized Average Width (PINAW), indicating strong reliability and superior capability in capturing uncertainty. This demonstrated that the interval prediction results of the proposed model could help decision-makers better understand and respond to the uncertainty and variability in the data. [Conclusion] The proposed CPO-CNN-BiLSTM point prediction model and the CPO-CNN-BiLSTM-NKDE interval prediction model effectively address the challenges posed by the spatial-temporal complexity of monthly runoff sequences and the uncertainty of monthly runoff point predictions. This provides new ideas for monthly runoff prediction and offers useful reference for fields such as wind speed and solar irradiance forecasting.