[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.

[Objective] Most existing studies on the response relationship between runoff variations and meteorological drought and flood characteristics focus on annual, seasonal, monthly, or weekly scales. This study aims to clarify the quantitative response relationship between meteorological drought and flood characteristics at the daily scale and runoff in the Jialing River Basin, and to effectively evaluate the impact of extreme meteorological drought and flood events on the flow process. [Methods] Based on long-term daily precipitation and flow data from 1989 to 2022, this study employed the SWAP index method and run theory to identify meteorological drought and flood events at the daily scale in the Jialing River Basin. Traditional multiple regression and emerging machine learning models were compared to simulate the internal relationship between meteorological drought and flood characteristics and flow change, revealing the response of runoff variation to drought and flood characteristics. [Results] The results showed that from 1989 to 2022, a total of 68 meteorological drought events occurred in the Jialing River Basin, leading to an average reduction of 48.25% in flow at the Beibei station. Compared to drought duration and intensity, the timing of drought events had a more significant impact on runoff variation and was the primary controlling factor influencing runoff variation. The support vector regression model considering only this factor could more accurately evaluate the change rate of flow caused by drought. During the same period, 40 meteorological flood events occurred in the Jialing River Basin, leading to an average increase of 130.46% in flow at the Beibei station. The accumulated precipitation before the flood peak had the greatest impact on the change rate of flow, and the timing of maximum precipitation before the flood peak had the greatest impact on the timing of flood peak. Multiple regression models were recommended to evaluate the response relationships between flood characteristic factors and the change rate of flow, as well as the timing of flood peak. To evaluate the impact of flood events on peak flow, the random forest model was recommended. The accumulated precipitation before the flood peak was the primary controlling factor influencing peak flow variation. [Conclusion] This study innovatively explores the response relationship between meteorological drought and flood characteristics at the daily scale and runoff variation in the river basin. The findings indicate that emerging machine learning models, such as support vector machines and random forests, can effectively simulate the complex mechanisms through which meteorological drought and flood events affect runoff in the river basin. This has significant implications for the scientific evaluation and prediction of the impact of extreme climate events on runoff characteristics.

[Objective] This study aims to conduct an in-depth analysis of the spatiotemporal variation characteristics of green water resource consumption and utilization efficiency in Jiangxi Province over the past two decades (2001-2020) to provide scientific support for regional water resource management and agricultural policy making. [Methods] We calculated green water utilization efficiency using two key datasets: (1) the transpiration-to-evapotranspiration ratio dataset (2001-2020) from China’s National Ecological Science Data Center, and (2) annual net primary productivity estimates derived from the MYD17A3H.006 remote sensing product. The Mann-Kendall trend analysis method was then applied to quantitatively evaluate the spatiotemporal trends in green water consumption and utilization efficiency across Jiangxi Province. [Results] From 2001 to 2020, total green water consumption in Jiangxi Province ranged from 796 to 965 mm, with an average of 887.75 mm, showing a significant downward trend at a rate of 5.26 mm/a. Productive green water consumption ranged from 519.12 to 692.53 mm, averaging 614.95 mm, and also showed a clear decreasing trend at 1.73 mm/a. Non-productive green water consumption ranged from 230.64 to 356.30 mm, with an average of 272.79 mm, showing a relatively significant downward trend at 3.53 mm/a. The annual green water utilization efficiency ranged from 0.67 to 0.81 g C/(kg H₂O), with a multi-year average of 0.74 g C/(kg H₂O), demonstrating a significant increasing trend at 0.006 7 g C/((kg H₂O)·a). Spatially, both total green water consumption and productive green water consumption exhibited a distribution pattern of higher values in the south and lower values in the north, with the highest observed in Ganzhou. Productive green water consumption showed an increasing trend around the Poyang Lake area, while most other regions exhibited decreasing trends. Total green water consumption showed an extremely significant decreasing trend across all regions, except in parts of Jiujiang and Jingdezhen. Across 92.70% of the province, green water utilization efficiency exhibited varying degrees of increase, with Ji’an and Yichun showing extremely significant improvements. [Conclusion] The decline in total green water consumption and the improvement in its utilization efficiency demonstrate notable achievements in water resources management and water-saving practices in Jiangxi Province. Future efforts should focus on optimizing water allocation, promoting water-saving irrigation technologies, and adopting high-efficiency cultivation practices to further enhance green water utilization efficiency in response to challenges posed by climate change and human activities.

[Objective] Danjiangkou Reservoir is a core project of the flood control system in the Hanjiang River Basin and a strategic water source for the Middle Route of the South-to-North Water Diversion Project, and its flood season staging schemes directly affect its flood control safety and inter-basin water transfer efficiency. Since its operation, Danjiangkou Reservoir has maintained an annual full storage rate of only 11%. How to improve this rate while ensuring flood safety remains an urgent issue. Existing research on flood season staging mainly focuses on statistical methods, with insufficient attention to the temporal characteristics of typical rainy seasons such as the plum rainy season in the middle-lower Yangtze River and the autumn rainfall in West China. This study aims to provide a theoretical basis and technical support for the operation and management of Danjiangkou Reservoir. [Methods] Based on daily inflow data of Danjiangkou Reservoir from 1961 to 2023, this study conducted preliminary flood season staging analysis using runoff statistical analysis, annual maximum flood analysis, mean change-point analysis, and vector statistical methods. [Results] The results obtained from different methods were relatively consistent. For safety reasons, the flood season was preliminarily divided into three stages. A further comprehensive analysis integrating the temporal patterns of the plum rain and West China autumn rainfall was conducted. The results showed that both exhibited significant temporal and periodic characteristics. From 1951 to 2023, the plum rain in the middle and lower reaches of the Yangtze River began as early as May 25 and ended as late as August 8. From 1961 to 2023, autumn rainfall in West China began as early as August 21 and lasted until November 30. During the plum rain period, when water levels in the middle-lower Hanjiang River were relatively high, any coincidental occurrence between floods from Danjiangkou and downstream floods may trigger flooding in the Hanjiang River Basin. After the plum rainy season ended, the flood control pressure in the middle-lower Hanjiang River decreased, allowing the Danjiangkou Reservoir to gradually release reserved flood control storage capacity. The autumn rainfall in West China started in late August in Sichuan and gradually moved eastward toward the Qinling Mountains, aligning with the autumn flood season. The autumn rainfall in West China directly affected Danjiangkou Reservoir’s storage conditions. During flood events, reservoir regulation must balance flood safety with increasing the full storage rate to fully utilize flood resources. In full consideration of both the plum rain and West China autumn rainfall, the flood season of Danjiangkou Reservoir was ultimately divided into three stages: summer flood season from June 21 to August 10, transition period from August 11 to August 31, and autumn flood season from September 1 to October 10. [Conclusion] Based on meteorological forecasts of end dates of plum rain and West China autumn rainfall, the flood limit water level for the summer flood season can be gradually raised to the level of the autumn flood season. This approach enables early water storage and improves both water resource utilization efficiency and the reservoir’s full storage rate. The research findings provide a theoretical basis for operation and scheduling decisions for Danjiangkou Reservoir.

[Objective] This study focuses on the Yangtze River Economic Belt and constructs a water resources carrying capacity evaluation system that covers four subsystems: water resources, society, economy and ecological environment. The aim is to reveal the current status and future development trend of water resources carrying capacity in the Yangtze River Economic Belt, and to provide a scientific basis and decision-making reference for the rational planning and utilization of water resources, the adjustment and optimization of industrial structure, and ecological environment protection within the region. [Methods] The spatiotemporal evolution characteristics of water resources carrying capacity in the Yangtze River Economic Belt from 2012 to 2021 were analyzed using the TOPSIS method and the standard deviation ellipse method. Combined with the coupling coordination degree model, the coordinated development level among the subsystems within the water resources carrying capacity system was further investigated. To better understand the future development trend of water resources carrying capacity, the grey prediction model was applied to predict its trend over the next five years. [Results] The study revealed the dynamic changes in water resources carrying capacity in the Yangtze River Economic Belt during the study period, along with its spatial distribution characteristics and evolution trends. Between 2012 and 2021, the water resources carrying capacity showed an overall fluctuating upward trend, gradually improving from an alert state to a good state, indicating a significant enhancement in the region’s water resources carrying capacity. Spatially, the center of water resources carrying capacity shifted southwestward toward areas with relatively lower capacity, which may be related to regional economic development patterns, industrial restructuring, and differences in water use efficiency. Regarding system coupling and coordination, except for 2012, the coupling degree between subsystems reached a high coupling stage, and the coupling coordination evaluation gradually shifted from near disorder to good coordination, demonstrating continuously improving coordinated development and enhanced synergy among the subsystems. Analysis of influencing factors identified the proportion of tertiary industry, urbanization rate, and per capita daily domestic water consumption as the three factors most strongly correlated with water resources carrying capacity. Changes in these factors significantly affected its increase or decrease. The water resources carrying capacity was projected to show a positive development trend over the next five years. [Conclusions] It is recommended that the upstream areas develop water-saving irrigation, control fertilizer usage, and enhance urbanization levels; the midstream areas develop reclaimed water use, strengthen sewage treatment, and accelerate industrial transformation; and the downstream areas control population growth, promote water conservation and environmental protection, restore ecosystems, and increase forest coverage. The research findings provide a valuable scientific basis for the efficient management and utilization of water resources in the Yangtze River Economic Belt,as well as for promoting the coordinated development of the regional economy and society,and for achieving sustainable development goals in the region.

[Objective] Taking the Shiyang River Basin, a typical arid inland river basin, as the study area, this study aims to explore the spatial distribution and matching patterns of agricultural water and soil resources under different scenarios of future land use change, identify the supply-demand imbalance and its causes in the river basin, and provide support for the planning and decision-making of sustainable agricultural development at the river basin scale. [Methods] Using the FLUS model, this study simulated the spatial patterns of land use of the Shiyang River Basin in 2035 under three scenarios: cropland protection, natural development, and ecological conservation. By introducing the agricultural water and soil resource equivalent coefficient, this study established a matching assessment model for future agricultural water and soil resources in the Shiyang River Basin. Combined with the water yield module of the InVEST model, this study predicted spatiotemporal variations in water yield under three scenarios in 2035 and evaluated the spatiotemporal matching relationships of agricultural water and soil resources in the Shiyang River Basin. [Results] (1) All seven county-level administrative regions in the Shiyang River Basin showed varying degrees of severe water shortage, indicating an overall imbalance in agricultural water and soil resources, with water supply unable to meet the demand of cropland-based agricultural production. (2) The spatial pattern of water and soil resource matching in the Shiyang River Basin was generally better in the west than in the east of the basin. The Gini coefficient ranged from 0.2 to 0.3, showing a slightly increasing trend but still indicating a relatively balanced condition. The average water and soil resource matching coefficient was projected to be 797 m³/hm² in 2035 under different scenarios, compared to 640 m³/hm² in 2020, showing an overall improvement. (3) Although water yield increased to some extent within each county-level region under different scenarios in 2035, the imbalance in actual water utilization and distribution continued to deepen over time. The continuous expansion of cropland under the natural development and cropland protection scenarios made it more difficult to balance the supply and demand of agricultural water and soil resources in Gulang County, Minqin County, and Liangzhou District. [Conclusion] For regions with poor balance between precipitation and evaporation, such as Liangzhou District, a typical resource-based water-scarce region, it is recommended to alleviate water shortage through interregional water transfer and the introduction of external water sources. In regions where agricultural development is dominant, it is advisable to accelerate agricultural modernization, reduce water consumption per hectare, and adjust crop structures. For regions with poor matching between water and soil resources, such as Minqin County, where both resource-based and engineering-related water scarcity coexist, it is proposed to address the imbalance through interregional water diversion and transfer, systematically improving the efficiency of water resource development and utilization.

[Objectives] This study aims to analyze the applicability of existing precipitation, temperature, and runoff data in data-scarce regions, and to develop and evaluate a deep learning hybrid model driven by multi-source information for improving the accuracy of monthly runoff forecasting. [Methods] Based on historical precipitation, temperature, and runoff sequences from the Yulongkashi River, a Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (CNN-BiGRU-Attention) model was developed. An Improved Particle Swarm Optimization (IPSO) algorithm was used to optimize this model, forming the IPSO-CNN-BiGRU-Attention hybrid model. The performance of this model was compared with that of the Gated Recurrent Unit (GRU) model and the ABCD water balance model. [Results] The IPSO-CNN-BiGRU-Attention model that incorporated precipitation and temperature data overall outperformed the CNN-BiGRU-Attention and GRU models, showing better agreement with the observed values. As the predication period increased, the proposed model achieved a root mean square error (RMSE) of 2.11 m³/s, a mean absolute error (MAE) of 1.32 m³/s, a mean absolute percentage error (MAPE) of 73.76%, and a Nash-Sutcliffe efficiency (NSE) coefficient of 0.94. The highest forecast accuracy was observed in the first three months. [Conclusions] The IPSO-CNN-BiGRU-Attention model effectively integrates precipitation, temperature, and runoff information, significantly enhancing the accuracy of monthly runoff forecasts in data-scarce regions. The model demonstrates robust performance across different forecast horizons, particularly suitable for short-term predictions of 1-3 months. This approach offers a practical and reliable tool for hydrological forecasting and flood control/drought management in data-scarce basins.

[Objectives] Identification of runoff components is a key aspect of hydrological analysis and is crucial for understanding the evolution patterns of watershed water resources. Traditional runoff component models are often constructed based on the criterion of maximizing the extraction accuracy of deterministic components for the runoff series of a given length. However, a unified criterion for selecting model forms that adapt to variations in runoff series length over time is still lacking, making it difficult to determine the types of runoff components and the order of their separation during modeling. To address this, this study proposes a selection criterion for runoff component models based on the balance between benefits and risks. [Methods] Based on the diagnosis and quantitative description of evolution characteristics such as mutations, trends, and periodicities using time-series variability detection methods—the Mann-Kendall test, sliding T-test, Pettitt test, Standard Normal Homogeneity test, Buishand test, and periodogram—different forms of linear superposition models were developed by combinations and extraction sequences of the identified components, such as mutation, trend, and periodicity. These models were then employed to dynamically identify the components of runoff sequences with varying lengths. The accuracy of deterministic component identification was used to represent the “benefits” achieved by the model in runoff component recognition, while the magnitude of fluctuations in model accuracy under varying runoff sequences (i.e., stability) was regarded as the “risk”. A weighting coefficient representing the decision-maker’s preferences was introduced as a balancing variable to construct a benefit-risk balance indicator. Subsequently, runoff component models were optimized based on the criterion of minimizing this benefit-risk balance indicator. [Results] Using the runoff sequence from 1956 to 2010 at the Pingshan Station on the lower reaches of the Jinsha River as a case study, variable-length runoff sequences (with sample sizes ranging from 30 to 55) were constructed, starting from 1956 and ending in any year from 1986 to 2010. Runoff component identification was conducted under different model forms, and the proposed benefit-risk balance criterion was applied for model selection analysis. The results indicated mutual offsetting among components such as mutations, trends, and periodicities in the runoff sequence, and the same runoff sequence could be characterized by multiple models, each representing distinct compositional forms of runoff components. Runoff component identification was jointly influenced by both the model form and the sequence length; models with higher identification accuracy exhibited relatively lower stability when responding to changes in sequence length. For instance, models incorporating periodic components demonstrated superior fitting accuracy compared to those containing only trend or mutation terms, which in turn outperformed multi-year average models, while the stability of accuracy changes followed the opposite trend. If the decision-making objective was to achieve a more adequate fitting, models that sequentially separate mutations and periodic components are prioritized; conversely, if the objective was to maintain more stable accuracy with varying sequence lengths, models that identify only mutation or trend terms were more advantageous. [Conclusions] A novel approach is proposed in this study for selecting component models of variable-length runoff sequences by balancing identification accuracy (benefit) and stability (risk). Both the accuracy and stability indicators proposed in the criterion can be flexibly defined according to decision-making needs, facilitating decision-makers in comprehensively considering their preferences for model accuracy and stability under varying conditions to optimize model selection.

[Objectives] With the socioeconomic development, conflicts among the population, water resources, and the environment have become increasingly prominent. Conducting research on water quality and quantity in rivers that flow through urban areas and serve functions such as water supply and irrigation, and implementing rational scheduling, is of significance for ensuring a healthy aquatic ecosystem and enhancing the well-being of local residents. [Methods] The Nansu River Basin was selected as the research area. A one-dimensional hydrodynamic-water environment coupled MIKE11 model was constructed, utilizing chemical oxygen demand (COD) and ammonia nitrogen (NH₃-N) as key indicators. The external boundary conditions for the hydrodynamic module were defined by upstream inflow and downstream outflow, with observed hydrological data serving as model inputs. For the water environment module, the boundary conditions were established based on the water environmental characteristics at the river boundaries and pollutant discharge data entering the river. [Results] The water environmental capacity (WEC) refers to the maximum permissible pollutant load that a water body can assimilate per unit time under specified water domain boundaries, hydrological conditions, regulated sewage discharge modes, and predefined water quality targets. The monthly average WEC for COD and NH₃-N showed a consistent pattern, with the highest capacity observed during the high-flow season, followed by the normal-flow season, and the lowest during the low-flow season. Water quality in the Nansu River deteriorated rapidly during the early flood season. To improve water quality, seven scheduling schemes were proposed by addressing two key aspects: controlling pollutant inflow from tributaries and increasing mainstream flow. [Conclusions] Improving water quality requires intervention in two primary areas: controlling pollutant inflow from tributaries and increasing the flow of the main stream. Based on the actual conditions of the basin and a comparison of seven regulation schemes, the Oupugou tributary is identified as the primary source of pollution affecting the mainstream. While both approaches—pollutant inflow control and mainstream flow increase—can achieve water quality improvement, the effect of pollution control is more significant than that of flow regulation. According to the comparative analysis of the scheduling schemes, the optimal scheme for improving water quality is to close the sluice gates of the Oupugou tributary to prevent pollutant inflow, and to moderately regulate water flow to further improve water quality.

[Objectives] Satellite altimetry has become a crucial method for monitoring lake water levels, yet significant challenges remain in its application to small lakes, particularly in complex urban environments. Currently, limited studies explore the effectiveness of satellite altimetry for monitoring variations of urban lake water levels. Using East Lake in Wuhan as a case study, this study evaluates the quality of Jason-3 satellite altimetry data, aiming to validate the capability of satellite altimetry in monitoring urban lake water level variations. [Methods] Based on the Jason-3 Sensor Geophysical Data Record (SGDR) products from 2017 to 2022, this study used two key parameters—pulse peakiness and waveform width—to first analyze the altimetry waveform characteristics of East Lake. In addition to the original range observations and ICE-retracked ranges provided by SGDR products, this study applied the Offset Center of Gravity (OCOG) and threshold methods for waveform retracking. Among them, the threshold retracking method selected eight threshold levels ranging from 20% to 90% (in 10% increments) to analyze the retracking performance under different thresholds. A robust coarse elimination strategy based on the Median Absolute Deviation (MAD) was employed to eliminate outliers from the water level observation data, followed by the calculation of periodic average water levels to construct the lake water level time series. To evaluate the quality of water level data by different methods, the range and standard deviation of water levels in each period, as well as the number of invalid periods, were statistically analyzed. Additionally, the accuracy of the results using different methods was verified using the measured data from hydrological stations. Finally, meteorological data (precipitation, evaporation) and a water balance model were integrated to quantify the contributions of natural and anthropogenic factors to East Lake’s water level variations. [Results] (1) Statistical analysis of pulse peakiness and waveform width from the lake surface altimetry echoes revealed that approximately 50% of East Lake’s waveforms exhibited specular reflections with distinct sharp peaks, while about 30% displayed complex shapes containing two or more peaks. (2) The results of accuracy validation using the on-site measured data of water levels showed that the 50% threshold retracking method achieved optimal performance, with a root mean square error (RMSE) of 0.108 m and a correlation coefficient of 0.87. (3) Based on the 50% threshold retracking method, and using Jason-3 data, the water level time series of East Lake from September 2017 to February 2022 was established. The results demonstrated that the lake water level remained stable around 19.5 m during this period, with annual fluctuations <0.5 m, monthly variations <0.2 m, and no pronounced seasonal pattern. Although precipitation was the primary water source, water levels showed extremely low correlation with precipitation (R=0.007), and weak negative correlation with evaporation (R=-0.44). According to the analysis of water balance, artificial regulation played a key role in the water level variations of East Lake. [Conclusions] (1) Jason-3 satellite altimetry data can effectively monitor urban lake water level variations, but requires careful data processing, including waveform retracking and outlier elimination. (2) Despite complex waveforms over urban lakes, retracking methods significantly improve altimetry accuracy. Compared with waveform retracking methods such as OCOG, ICE, and threshold method, the 50% threshold method is more suitable for urban lakes.

[Objectives] To reveal the spatiotemporal characteristics of extreme precipitation from 1981 to 2020 in the southern Gaoligong Mountain(S-GLG) and explore its relationship with strong ENSO events, this study analyses the trends of five extreme precipitation indices (EPIs) and their responses to large-scale sea surface temperature anomalies, such as the Oceanic Niño Index (ONI) and the Dipole Mode Index (DMI), providing a scientific basis for regional drought risk assessment and water resource management. [Methods] Using daily precipitation data from 8 meteorological stations, this study selected five EPIs: total wet-day precipitation (PTOT), maximum consecutive dry days (CDD), maximum 1-day precipitation (RX1day), number of heavy precipitation days (R10mm), and extreme precipitation intensity (SDII). Innovative trend analysis (ITA) and linear regression (LR) were used to analyze long-term trends, and composite analysis was employed to examine the impact of ENSO events (represented by ONI and DMI) on extreme precipitation. Seasonal-scale correlation analysis was conducted to distinguish the response differences between the western and eastern slopes. [Results] The results showed that except for a significant increase in CDD (3.9 d/(10 a) on the western slope and 0.7 d/(10 a) on the eastern slope), other EPIs exhibited decreasing trends, with PTOT decreasing most significantly (39.9 mm/(10 a) on the western slope and 46.1 mm/(10 a) on the eastern slope), indicating an intensifying drought risk in the region. ENSO correlations revealed weak to moderate negative relationships between extreme precipitation and ONI (p<0.1). During positive ONI phases (El Niño-like conditions), there was a higher probability of reduced precipitation during the rainy season. Additionally, the influence of DMI showed phase-dependent negative correlations, but with lower statistical significance. Regional seasonal differences were evident. The western slope showed a stronger negative correlation between rainy-season PTOT and CWD and simultaneous ONI during summer and autumn (r=-0.46 to -0.52), while the eastern slope exhibited a more pronounced lagged response of corresponding indices to ONI in the previous autumn and winter (r=-0.33 to -0.38), potentially indicating that topography may modulate the transmission of ENSO signals across the region. [Conclusions] The southern Gaoligong Mountain is experiencing a “drying” trend in extreme precipitation, with ENSO events (especially ONI) serving as key driving factors. Innovative findings include: (1) the first quantitative demonstration of seasonal response differences to ENSO between the western and eastern slopes, providing key parameters for improving local climate models; and (2) the proposal that early-stage ONI tracking may serve as a potential indicator for regional extreme precipitation prediction. These research findings provide important guidance for developing climate adaptation strategies in the region of Hengduan Mountains.

[Objectives] This study aims to overcome the limitations of traditional static evaluation methods by developing a multidimensional assessment framework for water resource carrying capacity with spatiotemporal continuity. It seeks to reveal the evolution patterns of regional water resources carrying capacity and propose optimized regulation schemes. [Methods] A dynamic-static analytical framework combining principal component analysis (PCA) and system dynamics (SD) modeling was applied, with Qingyang City in Gansu Province—an area relatively short on water resources—as the study area. First, using data from 21 indicators from 2012 to 2022, PCA was used to extract principal components (cumulative variance contribution rate >85%) to establish a comprehensive evaluation system for water resources carrying capacity and identify key influencing factors. Subsequently, a complex dynamic model of water resources system was established by dividing the system into socioeconomic, water supply-demand, and ecological subsystems. The dynamic changes in water supply and demand under different development scenarios were simulated. Four optimization schemes were designed: status quo development (baseline), water-saving, wastewater treatment, and integrated coordinated development. Their optimization effects on regional water resources carrying capacity were evaluated from the perspectives of water demand control, water supply efficiency improvement, and coordinated governance. [Results] (1) The water resources carrying capacity of Qingyang City significantly declined, with an annual average decrease rate of 18.78% from 2015 to 2022. PCA revealed that socioeconomic development (population growth rate, GDP per capita), water resource allocation efficiency (crude oil processing volume, water resources per capita), and ecological development level (green coverage rate in built-up areas) were the key driving factors, contributing 35.2%, 28.6%, and 19.3% to the principal component loadings, respectively. (2) Dynamic simulations showed that under the status quo development scheme (scheme 1), water shortage in 2035 increased by 47.8% compared to the baseline year (2012), with a supply-demand gap expanding to 123 million m³. The water-saving scheme (scheme 2) reduced the shortage by 11.9% through improved reuse rates, but due to the inflexible growth in water demand, the imbalance remained significant. The wastewater treatment scheme (scheme 3) reduced water shortage by 15.1% by increasing reuse rate to 55%, demonstrating a 3.2-percentage-point greater improvement compared to scheme 2. The integrated coordinated development scheme (scheme 4) implemented a synergistic “water-saving and pollution-control” strategy, optimizing demand-side control (improving industrial water-saving and agricultural irrigation efficiency) and enhancing supply-side circulation (wastewater reuse rate at 60%). This ultimately reduced the water shortage in 2035 by 16.7% compared to scheme 1, lowered total water demand by 19.4%, and narrowed the supply-demand gap to 51 million m³. [Conclusions] This study innovatively establishes an analytical paradigm integrating “historical diagnosis, dynamic early warning, and strategy optimization.” The degradation of water resources carrying capacity in oil and gas resource-based cities is essentially a manifestation of the imbalance between energy development, economic growth, and ecological protection. An integrated development strategy that includes water-saving, pollution control, and economic adjustments proves effective in alleviating water resource pressure through dual supply-demand adjustments. Future water management in Qingyang City requires curbing its current development trends promptly and regulating key guiding factors. Among the four projected schemes, the integrated coordinated development scheme performs optimally.

[Objective] Rainfall and runoff are two important hydrological variables in river basins, exhibiting the characteristic of random distribution. In-depth analysis of the relationship between rainfall and runoff holds significant importance for watershed flood risk management, water resource scheduling, and hydraulic engineering planning and design. [Methods] This study utilized the advantages of Copula functions in describing dependence relationships among random variables. First, a non-parametric kernel density estimation method was introduced, and four types of kernel density functions were used to characterize the marginal distributions of rainfall and runoff variables in the Fuchun River Basin. Subsequently, a bivariate Copula function was employed to establish a joint distribution model. Simulation performance for both marginal and joint distributions was validated using root mean square error (RMSE) and Euclidean distance. [Results] (1) By comparing the RMSEs between the estimated results using four kernel density functions (Gaussian, Uniform, Triangle, Epanechnikov) and the empirical frequencies of rainfall and runoff in the river basin, the Gaussian was found to have the smallest errors. The Gaussian was selected to estimate the marginal distributions of hydrological variables in the Fuchun River Basin, demonstrating higher simulation accuracy without relying on any distribution assumption.(2) By estimating the Kendall and Spearman rank correlation coefficients of the bivariate functions of Gaussian-Copula, t-Copula, Clayton-Copula, Frank-Copula, and Gumbel-Copula, and comparing them with the Kendall and Spearman rank correlation coefficients of the original observed data, it was found that Gaussian-Copula and Gumbel-Copula were closer to the observed data.(3) By calculating the Euclidean distance, the fitting performance of the Copula functions was evaluated. The Gumbel-Copula function was further selected as the optimal Copula function to describe the dependence structure between rainfall and runoff in the river basin. It revealed that the rainfall and runoff variables in the upper tail of the joint distribution were highly sensitive to changes, indicating strong correlation between annual rainfall and runoff extreme values in the river basin.(4) Further calculation of the upper tail correlation coefficient yielded a value of 0.758 3, indicating a 75.83% probability of both the annual rainfall and annual runoff reaching extreme values simultaneously. When an extreme value of rainfall occurs in a specific year in the river basin, runoff could be estimated based on the dependence relationship between rainfall and runoff in joint distribution established in this study. This provided a reference for flood risk management. [Conclusion] The Gaussian kernel function demonstrates excellent simulation performance for the marginal distributions of rainfall and runoff variables in the Fuchun River Basin, and the Gumbel-Copula function shows high goodness-of-fit for the joint distribution of rainfall and runoff. The findings of this study offer substantial implications for flood risk management and water resource scheduling in river basins, and provide a theoretical foundation for further research on rainfall-runoff stochastic simulation using Copula functions in the Fuchun River Basin. Additionally, they offer practical value for the calculation and analysis of hydrological variables and for the planning and design of hydraulic engineering in river basins.