最新刊期
- 摘要：Mountain-stream rivers in coastal areas commonly face issues such as low base flow during dry seasons, significant tidal fluctuations, and prominent internal nitrogen enrichment, making it difficult to achieve year-round stable water quality through a single water source ecological replenishment model. The mechanisms and temporal response patterns of dual water source collaborative nitrogen control remain unclear. Taking the Xichong River in Shenzhen as the study site, a MIKE21 two-dimensional hydrodynamic-water quality coupling model was constructed based on monthly hydrological, topographic, tidal, and water quality multi-source in-situ monitoring data from 2022. Model calibration and validation were completed using measured data of water level and total nitrogen across multiple cross-sections by season (Nash efficiency coefficient for water level >0.75, relative error for water quality simulation <12%). Four simulation scenarios were designed: no replenishment, single reservoir replenishment, reservoir-coupled single plant reclaimed water, and reservoir-coupled dual plant reclaimed water. From four dimensions—evolution characteristics of flow field stagnant zones, spatial distribution patterns of total nitrogen, water quality improvement efficiency, and proportion of compliant water bodies—quantitative analysis was conducted on the water environment improvement effects of different replenishment modes, exploring the temporal evolution patterns of continuous replenishment (1–3 months) and seasonal differences in water quality improvement. Results show that dual water source joint replenishment can synergistically enhance hydraulic displacement and low-concentration reclaimed water dilution effects. Under the optimal scenario (reservoir replenishment 0.10 m3/s + dual plant reclaimed water replenishment 0.12 m3/s), the comprehensive improvement rate of total nitrogen in the watershed reached 29.5%, and the proportion of surface water compliant with Class III standards increased from 22% to 80%. Water quality improvement efficacy is significantly positively correlated with replenishment duration, with continuous 3-month steady replenishment being the critical threshold for stable river water quality compliance. Due to low-temperature environments, monitored phenomena of sediment internal nitrogen release, and tidal backflow effects, water quality management efficacy during winter dry seasons is significantly lower than in flood and average flow periods. This study elucidates the temporal nitrogen control mechanism of dual water source replenishment in tidal-influenced mountain-stream rivers, providing technical support for long-term ecological replenishment scheduling and refined water quality management of similar coastal rivers in South China.关键词：MIKE21;ecological water replenishment;coastal mountain stream;total nitrogen (TN);water environment improvement;temporal response2|0|0更新时间：2026-10-13
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Spatial Differentiation of the Water-Saving Industry in Guangzhou Based on a Development Heat Index AI导读
摘要：Focusing on the water-saving industry in a broad sense, this study takes the 11 administrative districts of Guangzhou as units of analysis. Following the full-chain logic of "demand–supply–effectiveness," it constructs a Water-Saving Industry Development Heat Index system covering three dimensions: demand foundation, industrial vitality, and water-saving effectiveness. The demand foundation dimension includes per capita GDP, urbanization rate, and the share of the secondary industry; the industrial vitality dimension covers the number of water-saving enterprises, output value, revenue, and R&D expenditure; and the water-saving effectiveness dimension includes comprehensive water use per capita, water consumption per 10,000 yuan of GDP, industrial added value per unit of water consumption, leakage rate of the urban water supply network, reclaimed water utilization rate, effective utilization coefficient of farmland irrigation water, and the share of area covered by the comprehensive reform of agricultural water pricing. Data are drawn mainly from the Guangzhou Statistical Yearbook (2025), the Guangzhou Water Resources Report (2025), project-based enterprise surveys, and district-level self-assessments for water-saving society construction. Quantitative analysis and differentiated functional zoning are conducted using the entropy method, Spearman rank correlation, and Moran's I. To test the robustness of the index results, Monte Carlo perturbation (with ±20% uniform disturbances applied to the baseline weights) and bootstrap resampling are further employed. The results indicate that in 2024, the spatial imbalance of Guangzhou's water-saving industry was pronounced, showing an overall pattern of "prominent industrial highlands in the southeast, agriculture-related districts in the north providing bottom-line support, and strong demand but weak industry in the old urban cores." The comprehensive heat index (including agricultural water-saving indicators) exhibited significant positive spatial clustering (Moran's I 0.570, - 摘要：The mudflow from dam breaches has a wide impact range and significant destructive power, with flow characteristics distinct from clear water. Precisely elucidating its disaster-causing mechanisms holds practical significance. By constructing a parallel framework using OpenMP and establishing a constitutive model based on the Herschel-Bulkley rheology, the APPLIC (Approximated PLIC) method was employed to reconstruct the free surface. A self-developed three-dimensional mathematical model was utilized for viscoplastic dam breach hydrodynamic analysis, revealing the following findings: the current model's predictions align with physical experimental results; the impact force response of non-Newtonian fluids differs from Newtonian hydrostatic pressure variations, governed by equivalent gravity, and exhibits non-hydrostatic pressure effects. Inspired by seal whisker biologics, adjusting the shape to achieve the same drag coefficient reduced the peak impact force of circular cylinders compared to square cylinders by approximately 50%, while significantly diminishing impulse. The streamlined cylinder mitigated momentum changes, thereby reducing impulse. Multi-source data validated the model's reliability, elucidated non-hydrostatic pressure effects, linked biological inspiration with disaster prevention, and provided references for identifying dam breach disaster chains and mitigation applications.关键词：dam break;Herschel‑Bulkley model;approximated‑PLIC scheme;OpenMP technique;bio‑inspiration3|1|0更新时间：2026-10-12
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Evaluation of Waterlogging Defense Resilience Based on a Rainfall-Waterlogging Area Ratio Model AI导读
摘要：Building a resilient urban flood defense system is an effective measure against waterlogging disasters, but quantitative criteria for distinguishing between resilient and brittle defense are insufficient to evaluate the resilience of current and planned defense systems. Analogous to the stress-strain constitutive model of materials, a rainfall-inundation area ratio model for defense systems was developed, introducing a three-stage integrity criterion, characteristic load threshold, hardening discriminant index B, and yield-to-tensile strength ratio λ. Using small watersheds A (plain tidal river network area) and B (hilly area) in Guangzhou as case studies, a three-coupled model was employed to subject four sets of systems (current and planned) to 18 rainfall levels. The results showed hardening discriminant indices of 0.15, 0.60, 0.80, and 1.26 for the planned systems of watershed A and B, and the current systems of watershed A and B, respectively, corresponding to strong, moderate, weak, and brittle resilience. The planned system of watershed B failed at 170 mm with a yield-to-tensile strength ratio of 0.78. The curve morphology can quantitatively distinguish the resilient-brittle nature of defense systems, while the characteristic load and yield-to-tensile strength ratio provide a quantitative basis for planning, design, and super-standard defense measures.关键词：urban waterlogging;resilience evaluation;rainfall-waterlogging area ratio model;stress-strain analogy;tidal river network5|3|0更新时间：2026-10-10 - 摘要：In the context of increasingly frequent extreme rainstorms, extending flood forecast lead time while controlling the risk of missed flood-peak warnings has become a core requirement for flash-flood evacuation and reservoir pre-release in small and medium-sized basins. Although radar-based nowcasting and numerical weather prediction can provide basin-total rainfall in advance, a spatiotemporal scale mismatch persists between the areal rainfall totals supplied by meteorological models and the hourly series required by hydrological models. Conventional fixed hyetographs, which rely on a single discrete typical curve, tend to underestimate flood peaks and cannot represent the continuous risk spectrum from normal to extreme rainstorms. This study therefore proposes a dynamic quantile hyetograph method for flood forecasting. Two basins with sharply contrasting rainfall temporal structures were selected: the Jianshan Basin (1,578 km²; asymmetry coefficient A = 3.32; post-peak trailing) and the Hezikou Basin (1,031 km²; A = 0.43; pre-peak concentrated). They provided 15 and 21 flood events, respectively, yielding 36 events in total at a 1-h time step. Areal mean rainfall was derived by Thiessen polygon weighting; a 24-h rainfall sequence centred on the 6-h storm core was extracted, and each event was normalized into a dimensionless hyetograph. Empirical quantiles from 0.3 to 0.9 of the maximum cumulative rainfall over continuous 1–24 h durations were estimated to construct a duration-nested hyetograph library, and the incremental rainfall of each time step was allocated using a greedy pre-peak/post-peak strategy constrained by the basin-specific asymmetry structure. Runoff was simulated with a coupled saturation-excess and infiltration-excess module and routed by a time-invariant unit hydrograph, with all parameters held fixed so that differences could be attributed solely to the temporal rainfall distribution. A one-sided safety factor Kq, derived from the 90th percentile of the underestimation ratio, together with a 200-run Monte Carlo simulation, was used to quantify the underestimation risk induced by total-rainfall bias and quantile-selection error. The results show that the dynamic hyetographs adapt well to the two contrasting runoff concentration regimes. In the Jianshan Basin, all four static hyetographs gave a pass rate of 66.7%, whereas the dynamic P50 hyetograph raised it to 83.3% and reduced error dispersion by 42%. In the Hezikou Basin, the mismatched static hyetographs (fixed P80 and Huff) gave a pass rate of only 57.1%, with a mean underestimation of 22.3% and a maximum of 37.8%, whereas the dynamic P50 hyetograph reached 85.7% and the optimal q reduced the maximum underestimation to 7.1%. Variance decomposition indicates that total-rainfall error is the dominant uncertainty source (about 65%), while q-selection error accounts for about 35%; the mean probability of peak underestimation exceeding 20% is about 8.5%. The quantile q therefore acts as a risk-control variable that shifts the error distribution rather than as a simple accuracy on/off switch, revealing a clear trade-off between overestimation and missed warnings. By decoupling the regulation of rainfall temporal concentration from the total-rainfall safety margin, the proposed method provides a standardized interface from meteorological rainfall totals to hydrological model inputs and supports a three-tier rolling forecast product system comprising baseline, attention, and safety levels. It offers a low-cost and operationally feasible technical basis for flash-flood warning, reservoir pre-release, and rolling flood forecasting in small and medium-sized basins.关键词：flood forecasting;dynamic quantile hyetograph;meteorological–hydrological integration;independent validation;uncertainty;Jianshan Basin;Hezikou Basin3|4|0更新时间：2026-10-08
- 摘要：To address the systematic biases of the conventional Xin'anjiang model in forecasting complex flood events in tropical monsoon regions, this study proposes a physics-guided machine-learning framework for inflow flood forecasting at Gezhen Reservoir, aiming to improve forecasting accuracy and model interpretability. A total of 60 flood events were selected. The three-component Xin'anjiang model was calibrated using particle swarm optimization (PSO). Forty-four flood events before 2024 were used for model calibration and training, while 16 events occurring in and after 2024 were used for independent validation. XGBoost was employed to predict the residual errors of the Xin'anjiang model using model outputs, historical residuals, rainfall, and upstream reservoir outflow as input features. The predicted residuals were added to the Xin'anjiang model simulations to obtain the corrected inflow forecasts. Model comparison, input-feature ablation experiments, residual analysis, and SHAP analysis were conducted to evaluate model performance and interpret the contribution of different information sources. After residual correction, the mean Nash-Sutcliffe efficiency coefficient (NSE) during the validation period reached 0.958, with event-specific NSE values ranging from 0.82 to 0.98. The relative errors of flood peak discharge and flood volume were mostly within ±10%. The mean absolute error (MAE) and root mean square error (RMSE) of the residuals decreased from 130.49 m³/s and 193.25 m³/s to 84.51 m³/s and 147.92 m³/s, respectively. Ablation experiments showed that historical residuals were the most important inputs for error correction, whereas rainfall and upstream reservoir outflow alone provided limited improvement but contributed additional information when combined with residual features. SHAP analysis further indicated that recent residuals contributed most strongly to model predictions, reflecting the short-term persistence of hydrological-model errors.The Xin'anjiang model coupled with XGBoost residual correction effectively combines physical-process representation with nonlinear data-driven error learning. The proposed framework reduces systematic bias and improves the accuracy and stability of complex flood forecasting, while retaining reasonable interpretability. It can provide technical support for real-time reservoir inflow forecasting and flood-control operation in tropical monsoon regions.关键词：physics-guided machine learning;reservoir inflow flood forecasting;XGBoost;residual correction;SHAP interpretation;Xin'anjiang model15|33|0更新时间：2026-09-28
- 摘要：Medium-term runoff forecasting is essential for reservoir operation and scheduling, providing important support for water allocation, hydropower generation planning, flood control, and the coordinated operation of reservoir systems. However, natural runoff is affected by complex interactions among antecedent hydrological conditions, precipitation, atmospheric moisture, cloud processes, and other meteorological factors, resulting in pronounced nonlinearity, non-stationarity, and uncertainty. Existing forecasting methods often face three major limitations: insufficient utilization of multi-source meteorological information, interference among signals with different temporal frequencies, and high computational cost during repeated model calibration and parameter optimization. To address these problems, this study proposes a medium-term runoff forecasting method integrating meteorological information, ensemble empirical mode decomposition-Bayesian neural networks (EEMD-BNN), and multicore parallel computing. First, a meteorological information processing framework is developed by considering both the spatial distribution and temporal response characteristics of meteorological variables. Meteorological data from multiple grid points are spatially weighted, while lag analysis, correlation analysis, significance tests, and multicollinearity analysis are combined to identify effective predictors, thereby improving the representation of hydrological and meteorological interactions while reducing redundant information. Second, EEMD is introduced to jointly process runoff and meteorological series rather than decomposing runoff alone. The decomposed components are analyzed according to their frequency characteristics, and components of the same order with similar temporal scales are jointly fed into the corresponding BNN submodels. This component-wise modeling strategy separates mixed-frequency information into relatively stable subseries, reduces non-stationarity and random interference, and strengthens the ability of the forecasting model to capture relationships between runoff and meteorological signals at different temporal scales. The component forecasts are subsequently reconstructed to obtain the complete multi-step runoff process. Finally, considering the substantial computational burden associated with multiple parameter combinations and component models, independent computational tasks are executed in parallel on multiple processor cores for parameter optimization and model training, improving computational efficiency without changing the forecasting structure. The proposed method is evaluated for seven-day-ahead inflow forecasting at Hongjiadu Reservoir in the Wujiang River basin of southern China. Results show that it consistently outperforms conventional BNN and the ablation model in which only runoff is decomposed. On the test set, the proposed model achieves a correlation coefficient of 0.92 and a Nash-Sutcliffe efficiency coefficient of 0.90. Compared with the ablation model, decomposing the meteorological factors further reduces RMSE by 3.5%. In addition, training and parameter optimization time decreases from 11 010 s with one process to 523 s with 32 processes, corresponding to an approximately 21-fold speedup. The proposed method therefore improves the utilization of meteorological information, runoff forecasting accuracy, and computational efficiency, providing an effective approach for medium-term reservoir inflow forecasting and operational scheduling.关键词：Medium-term runoff forecasting;Multicore parallel computing;Ensemble empirical mode decomposition;Bayesian neural network;hydropower station10|7|0更新时间：2026-09-23
- 摘要：This study investigates the reduction in stormwater drainage capacity and the exacerbation of urban waterlogging caused by river-stage backwater and submerged outfalls in urbanized catchments within low-gradient river networks during extreme rainstorms. It aims to reveal the differences in drainage performance and river hydraulic responses of coupled sewer-river systems under free and submerged outflow conditions, thereby providing a basis for waterlogging mitigation and sluice-gate and dam operation. The Xiaotaihou River urbanized catchment in Beijing was selected as the study area. A coupled hydrological-hydrodynamic model, integrating surface rainfall-runoff processes, stormwater sewer conveyance, outfall discharge, and river routing, was developed using InfoWorks ICM. The model was calibrated and validated using four observed rainfall-discharge events. Six 3-h design storms with return periods of 2, 5, 10, 20, 50, and 100 years were simulated. With rainfall forcing, underlying-surface conditions, and sewer and river parameters held constant, two idealized river boundary scenarios, namely free outflow and fully submerged outflow, were established to quantify the effects of high river-stage backwater. During both the calibration and validation periods, the Nash-Sutcliffe efficiency (NSE) exceeded 0.70 and the coefficient of determination (R²) exceeded 0.80, indicating satisfactory model performance. For return periods of 20 years or less, the difference in inundated area between the two scenarios was below 2%. The 50-year storm was identified as the critical return period for catchment hydraulic response. Compared with free outflow, submerged outflow increased the inundated area with depths of 0.5–1.0 m from 3.30 km² to 4.06 km², representing an increase of 23%, and enlarged the area with depths of 0.15–0.5 m by approximately 1.30 km². Peak discharges at representative upstream, middle, and downstream outfalls decreased by 8.42%, 3.21%, and 1.57%, respectively. In addition, submerged outflow advanced the critical return period for abrupt water-level rises in the upper and middle reaches from 50–100 years under free outflow to 20–50 years.River-stage backwater has pronounced nonlinear, threshold-dependent, and spatially heterogeneous effects on catchment drainage and waterlogging control. For return periods of 20 years or less, the system retains sufficient conveyance and storage capacity, and outflow conditions have limited effects on waterlogging. Under the 50-year storm, submerged outflow substantially reduces sewer discharge capacity, expands moderate-to-deep inundation, and advances the threshold for abrupt water-level rises in the upper and middle reaches. Upstream outfalls are more sensitive to backwater effects and may experience drainage obstruction or reverse flow during extreme rainfall. Therefore, drainage modelling should couple river stages with outfall conditions. Pre-storm gate-dam drawdown to create storage can alleviate backwater effects and improve waterlogging control.关键词：Infoworks ICM;Free outflow;Submerged outflow;urban waterlogging;Small and medium-sized watersheds19|25|0更新时间：2026-09-23
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Discussion on the Determination of Roughness Coefficient for the Main Stream of the Huaihe River AI导读
摘要：The determination of roughness coefficients is an extremely important issue in flood stage estimation for large rivers. It directly affects the accuracy of computed flood levels, the rationality of flood control planning, and the safety assessment of levee systems. Over the past three decades, the roughness coefficients adopted in various channel regulation projects of the Huaihe River mainstream have largely followed previously obtained values. However, these values are no longer adequate for flood stage estimation in the middle reaches of the Huaihe River, where sediment inflow has decreased over a long period and channel cross-sections have enlarged considerably. Under such changed boundary conditions, the continued use of outdated roughness coefficients may lead to noticeable errors in computed flood stages. This study adopts a method combining qualitative analysis with quantitative verification. Qualitatively, it conducts a comparative analysis of roughness values for the seven major rivers in China, an analysis of riverbed coarsening caused by reduced sediment inflow, and an analysis of the superimposed cross-sectional evolution of the Huaihe River mainstream. These analyses provide a general reference for reasonable roughness ranges, reveal the response of bed material composition to the reduction in sediment inflow, and reflect the adjustment of cross-sectional geometry over time. Quantitatively, it incorporates a comparison of recent roughness calibration and verification results and performs verification using observed floods in 2020 and 2024. The observed floods represent typical events under current riverbed conditions and are used to test the rationality and reliability of the recommended values. Through the combination of qualitative analysis and quantitative verification, the recommended values are supported by physical mechanisms and tested against measured hydrological data. The results show that the cross-sections of the reach from Zhengyangguan to Fushan of the Huaihe River mainstream exhibit a trend of scouring and cross-sectional enlargement, and the roughness of the Huaihe River mainstream has increased to a certain extent. The recommended roughness coefficients for the main channel and floodplain of this reach are 0.021 0–0.024 5 and 0.033 5–0.039 0, respectively. For the reach from Zhengyangguan to Wuhe, the recommended values are 11%–16% higher than the currently used values of 0.0215 for the main channel and 0.0335 for the floodplain. These increases indicate that the hydraulic resistance of the Huaihe River mainstream has changed markedly under the combined effects of reduced sediment inflow, riverbed coarsening, and cross-sectional enlargement. The research results can provide references for future Huaihe River regulation, the construction of the Digital Twin Huaihe River Basin, and the regulation and management of other rivers. They can also serve as a technical basis for updating roughness parameters in flood forecasting, flood control operation, and riverbed evolution analysis.关键词：roughness coefficient;hydrodynamics;river channel evolution;inflow and sediment load;Huaihe River11|5|0更新时间：2026-09-23 -
Assessing Ecological Product Value Transformation Effect of Happy Rivers and Lakes Using PCA and AHP AI导读
摘要：The evaluation of ecological product value transformation effect serves as a fundamental prerequisite for the realization of ecological product value. Current research on happy rivers and lakes lacks specialized evaluation systems for the transformation of ecological product value, and the single weighting systems based on Principal Component Analysis (PCA) and Analytic Hierarchy Process (AHP) are deficient in robustness verification. Taking a typical river basin as the research carrier, this study constructs a PCA-AHP dual weighting system that integrates the advantages of subjective and objective weighting methods. Combined with the fuzzy comprehensive evaluation method, a comprehensive evaluation model for the transformation effect of ecological product value in the construction of happy rivers and lakes is established to systematically assess the temporal transformation effect of watershed ecological product value. Furthermore, the indicator contribution analysis method and elasticity analysis method are adopted to accurately identify the core influencing factors and highly sensitive indicators driving ecological product value transformation, and clarify the action intensity, action direction, and inter-annual fluctuation characteristics of each indicator. Based on the actual development conditions of the river basin, three differentiated development scenarios including baseline development, ecological priority, and industrial coordination are set to dynamically simulate and predict the development trend and optimal development path of ecological product value transformation in the Taoxi River Basin by 2030.The research results indicate that: (1) The evaluation results of ecological product value transformation effect obtained by PCA and AHP show good consistency, which verifies that the dual weighting integration model can effectively avoid the limitations of single weighting methods; (2) From 2019 to 2023, the transformation of ecological product value in the Taoxi River Basin remained in a good state with a steady upward trend; (3) The PCA-AHP dual weighting comprehensive evaluation model has low sensitivity to small weight fluctuations (0.1) at the criterion layer, presenting strong anti-interference ability and reliable calculation results, which is suitable for the long-term evaluation of watershed ecological product value transformation; (4) The proportion of industrial added value in GDP, tourist quantity, total tourism revenue, and per capita disposable income of rural permanent residents are the core influencing factors, whose influencing intensities show slight inter-annual fluctuations; (5) Multiple highly sensitive indicators exert promoting or inhibiting effects on ecological product value transformation. The per-unit public welfare forest area forestry loan discount and the proportion of agriculture, forestry, animal husbandry and fishery output value in GDP are the most sensitive indicators, both of which present significant promoting effects; (6) The industrial coordination scenario achieves the optimal effect of ecological product value transformation in the Taoxi River Basin in 2023, demonstrating that the coordination of ecological protection and industrial development is the key path to improve the efficiency of ecological product value transformation in the basin. This study provides a quantitative basis and decision-making reference for the realization of watershed ecological product value and the construction of happy rivers and lakes.关键词：happy rivers and lakes;ecological product value transformation effect;principal component analysis;analytic hierarchy process;sensitivity of index9|5|0更新时间：2026-09-23 - 摘要：Termite nests in earth-rock embankments are highly concealed and pose severe hazards: the main nest, galleries, and alate chambers can interconnect to form seepage channels, readily inducing piping, sliding, or even dam failure during flood season. Electrical impedance tomography (EIT) provides a novel, non-destructive approach for detecting such nests. The electrode layout directly controls the coverage and uniformity of the EIT sensitivity field and thus determines the accuracy of nest-structure identification. In this study, a two-dimensional embankment termite-nest model incorporating the main nest, galleries, and alate chambers was established on a COMSOL-MATLAB co-simulation platform. The model measures 0.6 m × 0.5 m, and the conductivities of the background soil and nest region are set to 0.002 S/m and 0.006 S/m, respectively, representing the low-moisture dry-season conditions typical of the Poyang Lake area. Six typical electrode layouts (S-1–S-6; 22–52 electrodes in total) were systematically compared: S-1 (peripheral boundary only), S-2 (internal uniform only), S-3 (W-shaped hybrid), S-4 (central-column-removed hybrid), S-5 (inner-column-removed hybrid), and S-6 (fully hybrid uniform layout combining S-1 and S-2). Forward simulation was performed with 10 mA adjacent excitation, and dynamic difference imaging was conducted using an L1-regularization algorithm. To mimic realistic measurement conditions, independent 60-dB Gaussian white noise was added to the boundary voltage data of both the empty and full fields. The image correlation coefficient (ICC) was used as the quantitative evaluation metric. Results show that the peripheral-only and W-shaped layouts (S-1 and S-3) yielded low ICCs of only about 0.53–0.60 and failed to resolve detailed structures, with S-3 exhibiting the most severe distortion because of its highly non-uniform sensitivity distribution. The internal-uniform and incomplete-hybrid layouts (S-2, S-4, and S-5) increased the ICC to 0.62–0.64 but still exhibited artifacts or structural discontinuities. In contrast, the fully hybrid uniform layout (S-6) achieved the highest ICC of 0.7728, an improvement of 20.4% over the second-best S-5 and more than 35% over the other schemes; the galleries, main nest, and alate chambers were all clearly distinguishable. Sensitivity-distribution analysis further revealed that sensitivity-field uniformity—quantified by the coefficient of variation (CV) and strongly negatively correlated with ICC—is the key mechanism underlying the differences in reconstruction quality. These findings demonstrate that the S-6 layout constrains the overall electric field through boundary electrodes while enhancing local sensitivity coverage through internal electrodes, thereby significantly improving EIT detection accuracy. This work provides an optimal grid-based electrode configuration for high-precision EIT detection of termite nests in embankments; its topology can be readily adapted to field deployment by combining surface electrodes along the dam crest with shallow grid electrodes on the slope.关键词：electrical impedance tomography (EIT);embankment termite nest;electrode layout;numerical simulation;image correlation coefficient9|5|0更新时间：2026-09-22
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Simulation Evaluation of the Influence of Basin Terrain Height on Extreme Rainfall Based on WRF AI导读
摘要：Basin topography can strongly modulate extreme rainfall by altering low-level moisture transport, convergence, and thermal stratification. To evaluate the influence of basin terrain height on extreme rainfall and its physical mechanisms, this study applies the WRF-ARW version 4.3.1 mesoscale model to the "8·16" extreme rainfall event that occurred over the Sichuan-Chongqing region in 2015. Eight terrain-height sensitivity experiments are designed, including the original-terrain control experiment (CTL), three terrain-lowering experiments, and four terrain-raising experiments based on a 1200 m reference height. NCEP reanalysis data are used as the initial and lateral boundary conditions, and CLDAS hourly precipitation data are used for model verification. The analysis combines precipitation verification indices, spatial and hourly rainfall evolution, 850 hPa water vapor transport, water vapor flux divergence, pseudo-equivalent potential temperature, and vertical velocity. The results show that the control experiment reasonably reproduces the observed northeast-southwest-oriented rainband and the main heavy-rainfall center in the central basin. The threat score exceeds 0.98 for precipitation above 0.1 mm, while the scores for the 10 mm, 25 mm, and 50 mm thresholds are approximately 0.50, 0.22, and 0.10, respectively, indicating usable skill for the main rainfall pattern but larger uncertainty for rainstorm-level precipitation. Terrain-height perturbations produce local differences in 24 h accumulated precipitation exceeding 80 mm. Terrain uplift generally enhances precipitation in the central and northeastern basin, weakens precipitation in the southern basin, and shifts the rainfall center above 250 mm southwestward from approximately (30.8°N, 105.8°E) in Ep1_6 to approximately (30.0°N, 105.2°E) in Ep1_2; no rainfall center above 250 mm appears in Ep1_1. Terrain height also affects the diurnal evolution of precipitation. The precipitation peak in CTL reaches 3.04 mm at 23:00 UTC on 16 August, while Ep1_1 shows an earlier double-peak structure and Em1_3 lags CTL by about 1 h. Mechanism analysis further shows that changes in basin terrain height alter the low-level moisture transport pathway and the location of 850 hPa water vapor convergence near the basin margins. These changes modify the horizontal gradient of pseudo-equivalent potential temperature in the lower and middle troposphere, thereby affecting stratification instability and the development of upward motion. As a result, the intensity, location, and timing of heavy precipitation are modulated by the terrain-height perturbations.These results suggest that basin terrain-height variation is an important factor affecting the intensity, location, and peak timing of this extreme rainfall event, and can provide a reference for terrain-sensitive simulation, forecast uncertainty assessment, and flood-risk warning in mountainous basin regions. They also highlight the need to represent basin terrain height carefully in numerical models used for operational rainfall forecasting.关键词：WRF model;basin terrain;terrain height;extreme rainfall;sensitivity experiment;water vapor convergence5|6|0更新时间：2026-09-22 - 摘要：In the field of embankment settlement prediction, the coupling mechanism between input and output time steps remains unclear, and traditional data-driven models suffer from significant error accumulation in long time-series prediction and insufficient consistency with physical mechanisms. To address these common problems, this study investigates the influencing factors of multi-step prediction accuracy for embankment settlement, aiming to provide a basis for selecting input-output time steps in settlement monitoring and safety early warning of embankments. A physics-informed long short-term memory (LSTM-PINN) model was constructed by embedding Terzaghi's one-dimensional consolidation equation into the loss function of a conventional LSTM network as a soft constraint. The model was trained and validated using continuous hourly automated monitoring data (17,520 valid samples) collected from the 26+200 cross-section of the Dongguan Levee in Guangdong Province from January 2024 to December 2025, covering crest settlement, upstream water level, air temperature, and rainfall. Cross-comparison experiments were conducted with five historical observation windows (12, 24, 48, 72, and 96 h) and five prediction steps (3, 6, 12, 24, and 48 h), and LSTM-PINN was benchmarked against LSTM, GRU, and Transformer models. The effects of the physical constraint weight and data noise intensity on model performance were further quantified.FindingsThe input time window has an optimal value: prediction accuracy improves as the window extends from 12 h to 72 h and declines beyond 72 h due to redundant information and noise. At the 72 h window, LSTM-PINN achieves the minimum RMSE of 0.137 mm, reducing RMSE by 17.4%, 22.0%, and 26.9% relative to LSTM, GRU, and Transformer, respectively. Prediction accuracy declines with increasing prediction step, but LSTM-PINN degrades much more slowly; for 24 h and 48 h predictions, its RMSE is reduced by 28.5% and 41.2% compared with LSTM, respectively, and its error growth rate is only 58.3% of that of the data-driven models. The optimal input-output step ratio ranges from 3:1 to 5:1; for example, the combinations of an input 12 h window with a 3 h prediction step and an input 72 h window with a 24 h prediction step are recommended for real-time and medium-to-long-term early warning, respectively. The physical constraint weight has an optimal interval of 0.05–0.20, with the best overall performance at 0.1. Under a noise intensity of 0.3 mm, the accuracy degradation of LSTM-PINN is 27.9%, markedly lower than the 46.8% of LSTM. Moreover, the settlement rate predicted by LSTM-PINN agrees well with the measurements (correlation coefficient of 0.996), reproducing the semi-diurnal tidal fluctuation superimposed on the long-term consolidation trend. Under the data conditions of this study, the proposed model can separate consolidation settlement from tidal elastic deformation to a reasonable extent and exhibits clear advantages in long time-series prediction. The findings can provide references for parameter optimization of embankment settlement monitoring and early warning systems. Since the validation was performed on a single monitoring cross-section, the applicability of the model to different embankment types, geological conditions, and working conditions needs further investigation.关键词：embankment settlement;multi-step prediction;physics-informed neural network;long short-term memory (LSTM);time step optimization;safety early warning9|7|0更新时间：2026-09-16
- 摘要：To reveal the long-term variation patterns and spatial differentiation characteristics of extreme precipitation under complex mountainous terrain conditions, this study used daily precipitation data from eight national meteorological stations in the Shaoguan region of northern Guangdong Province, China, over the 1965–2024 period; after quality control and interpolation, the dataset comprised 175,108 daily precipitation records. Eight extreme precipitation indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), namely RX1day, RX5day, R10mm, R25mm, R50mm, CWD, CDD, and PRCPTOT, were calculated, and linear trend analysis, the Mann–Kendall test with Sen's slope estimator, the moving t-test, and power spectral analysis were employed to analyze their spatiotemporal variations. The results show that the mean annual precipitation in the Shaoguan region was 1,659.3 mm during 1965–2024, exhibiting a weak increasing trend (Sen's slope = 3.90 mm/a; cumulative increase ≈ 234 mm over 60 years), although this trend was not statistically significant at the 95% confidence level (Z = 1.754, p = 0.079). The regional mean increased by 4.4%, from 1,623.5 mm in 1965–1994 to 1,695.2 mm in 1995–2024. Mean precipitation ranged from 1,423.6 mm in the 1960s (-14.2% anomaly) to 1,814.6 mm in 2020–2024 (9.4%). The intensity and frequency of extreme precipitation increased significantly. RX5day increased at 0.98 mm/a (Z = 2.838, p < 0.01; cumulative increase ≈ 59 mm), and R25mm and R50mm increased significantly at rates of 0.075 and 0.031 d/a, respectively; R10mm showed no significant trend. During 2020–2024, R50mm was 46.5% higher than its climatological average, and the regional mean number of rainstorm days reached 8.1 d, indicating a marked shift in precipitation structure toward extremes. Precipitation was concentrated in the April–September flood season, accounting for 71.3% of the annual total, with 45.6% in the pre-flood season (April–June) and 25.7% in the post-flood season (July–September), showing a pattern of more precipitation early and less late in the flood season. Spatially, precipitation was strongly controlled by topography: wet zones occurred in the southern and southwestern mountains (Xinfeng, 1,918.3 mm), whereas dry zones occurred in the northern basins (Lechang, 1,484.3 mm), with an inter-station difference of 434 mm (29.2%). R50mm at Xinfeng (8.0 d) was 2.2 times that at Lechang (3.6 d), and trends varied among stations: R50mm increased significantly at four stations (Qujiang, Shixing, Ruyuan, and Lechang) but showed a weak decrease at Wengyuan. Annual precipitation exhibited quasi-2–3-year and quasi-7–8-year periodic oscillations, and extreme precipitation underwent abrupt changes: R50mm increased from 5.1 d in 1965–2004 to 6.3 d in 2005–2024 (23.1%, p < 0.05), and extreme precipitation intensity increased abruptly around 2010. This intensification is thermodynamically consistent with the Clausius–Clapeyron relationship and closely related to the westward extension of the western North Pacific subtropical high and the phase shift of the Pacific Decadal Oscillation. Overall, although the annual precipitation trend in the Shaoguan region was not significant, the intensity and frequency of extreme precipitation, especially RX5day and R50mm, showed significant increasing trends closely related to the mountainous topography, implying an elevated risk of rainstorm and flood disasters. These results can provide a scientific reference for meteorological disaster risk assessment and flood control planning in the Shaoguan region.关键词：extreme precipitation;Mann–Kendall test;spatiotemporal differentiation;abrupt change;periodic oscillation;mountainous terrain;Shaoguan13|5|0更新时间：2026-09-16
- 摘要：Accurate next-day mean daily discharge forecasts are fundamental to reservoir operation, flood preparedness, water resources allocation, and ecological management, particularly in regulated basins within monsoon climate regions. However, operational forecasting in such environments faces persistent challenges, including limited historical records, incomplete characterization of antecedent hydrological states (e.g., soil moisture and baseflow), and the risk of data leakage inherent in conventional random cross-validation schemes. To address these limitations, this study proposes a robust forecasting framework based on the CatBoost gradient boosting algorithm for the Hengshan (II) Hydrological Station in the Meijiang River Basin. Using daily hydrometeorological and reservoir data from 2021 to 2025, we constructed a 33-dimensional feature set with clear physical interpretability, organized into four groups: discharge memory, antecedent rainfall and catchment wetness (including a calibrated Antecedent Precipitation Index), evaporative demand and seasonality, and aggregated reservoir storage dynamics. A rigorous causality constraint was enforced by shifting all predictors so that only information available by day t-1 was used to forecast discharge on day t. Following a 30-day initialization period, 1 795 valid daily samples were retained. Model development employed an expanding-window five-fold time-series cross-validation approach on data from 2021–2024, coupled with Bayesian hyperparameter optimization, while all 365 observations from 2025 were strictly reserved for independent testing. Comparative analysis against Persistence, Ridge Regression, ExtraTrees, and LightGBM benchmarks demonstrated the superiority of the fixed-parameter CatBoost model. On the independent test set, CatBoost achieved a Nash–Sutcliffe Efficiency (NSE) of 0.918, a Kling–Gupta Efficiency (KGE) of 0.956, a Root Mean Square Error (RMSE) of 69.5 m³/s, and a Mean Absolute Error (MAE) of 38.7 m³/s. Notably, its NSE exceeded those of LightGBM and ExtraTrees by 8.0% and 3.0%, respectively (corresponding to absolute NSE gains of 0.068 and 0.027), and its RMSE was 52.6% lower than that of the persistence benchmark. Feature ablation experiments elucidated the distinct roles of the feature groups: multiscale discharge-memory features established the predictive baseline, whereas rainfall and wetness variables contributed a significant NSE gain of 0.447. Crucially, the inclusion of reservoir-state features did not markedly alter annual NSE but substantially reduced MAE and Percentage Bias (PBIAS) during the dry season (PBIAS improved to -0.04%), highlighting their role in correcting low-flow biases. Bayesian optimization improved the mean cross-validation NSE from 0.772 ± 0.114 to 0.797 ± 0.083 and reduced fold-to-fold variability by 27.2%, yet it lowered the independent test NSE to 0.904, underscoring that optimization targeting development-period performance does not guarantee enhanced interannual generalization. Further uncertainty quantification via split-conformal prediction and out-of-fold residual analysis revealed pronounced seasonal heteroscedasticity; the mean 90% prediction interval width during the flood season was approximately 2.7 times that of the dry season. Although the model effectively captured the timing of most hydrograph limbs, its performance deteriorated for the 19 extremely high-flow days (peak NSE = 0.287), primarily because daily basin-average rainfall and aggregated storage metrics cannot represent sub-daily rainfall intensity, storm spatial heterogeneity, or individual reservoir release patterns. In conclusion, the proposed causality-constrained framework offers a reproducible and operationally feasible solution for next-day rolling forecasts in data-scarce, multi-reservoir basins. Nevertheless, for extreme flood events, the integration of higher-resolution radar rainfall data, soil moisture observations, and individual reservoir operation schedules remains essential for reliable risk assessment.关键词：daily average discharge forecasting;CatBoost;multiscale features;Bayesian optimization;time-series cross-validation;Meijiang River19|5|0更新时间：2026-09-14
- 摘要：Flood risk identification and emergency rescue scheduling for power distribution facilities in tidal river network areas are challenging under the combined effects of typhoon-induced rainfall, high-tide backwater, and storm surge. Distribution facilities located in low-lying urban areas, along river channels, and near drainage outlets are particularly vulnerable to compound flooding, while conventional warning methods based on fixed thresholds may have difficulty reflecting changes in facility safety margins under complex flood conditions. Therefore, an integrated method combining flood simulation, dynamic warning, emergency resource estimation, and rescue-route optimization was developed to support facility-level flood prevention and emergency response. Rainfall intensity, water-level rising rate, tidal-level deviation, and storm surge were comprehensively considered to establish a dynamic warning threshold model for identifying potentially affected distribution facilities. Taking Haizhu District of Guangzhou as a case study, a fully coupled river–surface–pipe network hydrodynamic model was applied to simulate the evolution of urban flooding under different combinations of rainfall and tidal boundary conditions. Four scenarios were designed, including a rainfall-only scenario (S1), a rainfall combined with astronomical high tide scenario (S2), a rainfall–high tide–storm surge compound scenario (S3), and an enhanced-rainfall compound sensitivity scenario (S4). Based on the simulated inundation conditions and identified risk facilities, the requirements for emergency rescue personnel, drainage equipment, and rescue vehicles were further estimated. In addition, road inundation depth was transformed into travel impedance to represent the influence of flooding on vehicle movement, and rescue routes were optimized under different flood-risk scenarios by considering the resulting traffic constraints. With the progressive superposition and intensification of flood-driving factors from S1 to S4, the effective inundated area increased from 2.25 km² to 11.58 km², indicating a marked expansion of the urban area affected by flooding. Meanwhile, the number of risk distribution facilities included in emergency resource assessment increased from 0 under S1 to 8 under S4. Compared with the conventional static-threshold method, the proposed dynamic warning threshold identified one additional early-warning facility under S3 and six additional facilities under S4. The results indicate that the dynamic threshold can provide earlier attention to facilities exposed to increasingly adverse compound flood conditions. Under the most severe scenario, S4, the estimated emergency response demand reached 32 rescue personnel, 13 pumping equipment, and 2 emergency vehicles. After road inundation was incorporated into the travel-impedance calculation, feasible rescue routes satisfying the specified traffic constraints could be obtained for all risk facilities under both S3 and S4, demonstrating the importance of considering flood-induced deterioration of road accessibility in emergency rescue scheduling. The proposed method integrates compound-flood simulation, dynamic warning of distribution facilities, emergency resource estimation, and rescue-route optimization into a continuous analysis framework. It can transform hydrodynamic simulation results into information directly related to facility warning, emergency preparedness, resource allocation, and rescue scheduling. The study provides technical support for flood-risk warning, emergency resource allocation, and emergency rescue decision-making for power distribution facilities in tidal river network areas, and offers a practical analytical approach for improving the flood-response capability of urban lifeline systems under compound typhoon-rainstorm and tidal conditions.关键词：urban lifeline safety;distribution facilities;compound flooding;dynamic warning threshold;rescue route optimization9|0|0更新时间：2026-09-10
- 摘要：The scientific protection and rational utilization of reservoir shorelines are directly related to the long-term stable exertion of comprehensive engineering benefits and the coordinated ecological and economic sustainable development of regional areas within the Pearl River Basin. The Baise Water Conservancy Project is one of the first large-scale directly-managed reservoirs in the Pearl River Basin to organize the systematic planning and formulation of reservoir area protection and utilization. At present, numerous large reservoirs distributed across the whole watershed are facing prominent practical obstacles in daily supervision. The prominent problems include insufficient systematic normative basis for standardized shoreline administration, as well as the long-term fragmented supervision mechanism that causes disjointed and isolated control measures between shoreline land zones and adjacent reservoir water areas. Such problems greatly restrict the realization of refined, integrated and holistic governance of reservoir land and water resources, and make it difficult to form consistent and effective long‑term supervision mechanisms suitable for complex trans-regional river basin management within the Pearl River Basin. Taking this typical project as the research object, this paper carries out systematic planning practice for reservoir shoreline protection and utilization in strict accordance with existing national industrial technical specifications, and fully integrates long-term on-site management experience, local ecological background conditions and the actual supervision demands within the reservoir area. Supported by the ArcGIS spatial analysis technology platform, this research adopts field investigation, remote sensing interpretation and spatial superposition analysis methods, and superimposes multi-source spatial datasets including ecologically sensitive areas and the national "Three Zones and Three Lines" spatial control achievements to conduct comprehensive systematic evaluation aimed at balanced ecological protection and resource allocation. The total reservoir shoreline length of 1189.59 km is reasonably divided into 216 independent shoreline functional zones, among which protected shoreline segments account for 84.18% of the total shoreline length. Breaking through the traditional separated management framework of land and water space, this study innovatively proposes operable integrated shoreline-water area control requirements. The research results provide a feasible implementation path to resolve the refined shoreline governance difficulties of directly-managed large reservoirs in the Pearl River Basin. Meanwhile, the research conclusions help promote the effective implementation of relevant industrial standards, continuously improve the standardized scientific management capacity of reservoir shorelines, and offer valuable technical references for similar reservoir shoreline planning practices in other river basins, which bears important practical significance, good promotional prospect and wide popularization and demonstration value.关键词：Baise Water Conservancy Project;protection and utilization;shoreline boundary;shoreline functional zones;control requirements11|0|0更新时间：2026-09-10
- 摘要：To address the issue of photovoltaic arrays in fish-photovoltaic complementary systems altering water body light conditions and affecting aquaculture ecology, a multi-dimensional coordinated optimization scheduling model was developed. This model balances algae light growth requirements, fish dissolved oxygen needs, energy storage charge-discharge characteristics, and power generation economic benefits. First, the photovoltaic power generation capacity and effective water surface light exposure area were calculated based on solar irradiation angles. Algae photosynthetic oxygen release was estimated using water body light conditions, and combined with fish survival dissolved oxygen thresholds, the activation and deactivation timing of oxygenation equipment was determined to compensate for natural oxygen deficiency. Then, with the optimization objective of maximizing system comprehensive benefits, dynamic adjustment of photovoltaic array tilt angles and optimal energy storage charge-discharge strategies were implemented. Under the strict ecological premise of ensuring normal fish growth, the synergistic maximization of photovoltaic power generation benefits and aquaculture benefits was achieved. Simulation results demonstrated that the proposed model effectively enhances system operational efficiency, increasing daily operational cycle comprehensive benefits by 38.85%. During the aquaculture cycle, stable photovoltaic power generation revenue can offset fish market price volatility risks, improving project returns by 4.63% and 7.36% under high and low fish price scenarios, respectively. This research provides effective theoretical support and technical references for the ecological, refined, and high-efficiency operation and scheduling of fish-photovoltaic complementary systems.关键词：fishery-photovoltaic integration;photovoltaic power generation;algal photosynthesis;dissolved oxygen in water;fish farming13|0|0更新时间：2026-09-07
- 摘要：The Songzikou Outlet ranks first in flow-sediment diversion among the three outlets of the Jingjiang River, and its variations exert profound impacts on the hydrological pattern of the Jingjiang-Dongting Lake water system. Based on long-sequence hydrological datasets spanning 1956–2025 from Zhicheng, Xinjiangkou and Shadaoguan hydrological stations, this study employs the Mann-Kendall trend test and Pettitt change-point test to systematically analyze the evolutionary characteristics of flow and sediment diversion in the eastern and western branches of the Songzikou River. The results reveal that over the past 70 years, under the condition of generally stable inflow from the main stem, both the eastern and western branches of the Songzikou River have experienced prominent phased attenuation in diverted flow and sediment. Annual runoff of the western branch (Xinjiangkou Station) and the eastern branch (Shadaoguan Station) shows an extremely significant decreasing trend, with abrupt changes occurring in 1991 and 1986, respectively; the eastern branch suffers an especially drastic runoff reduction of 50.7%. Annual sediment loads at the three stations drop sharply. The abrupt-change time of sediment load for the eastern branch (1992) precedes that of the main stem (2000), reflecting the high sensitivity of distributary channels to sediment regime shifts. In terms of intra-annual distribution, dry-season runoff presents a divergent pattern featuring increased runoff in the main stem and western branch versus a sharp decline in the eastern branch. Dry-season runoff at Zhicheng and Xinjiangkou stations increases remarkably by 43.1% and 162%, while that at Shadaoguan Station plummets by 90.8%. This phenomenon reflects the combined effects of water replenishment of the Three Gorges Reservoir and inlet degradation of the eastern branch, accompanied by flow shrinkage to varying degrees in other periods. Intra-annual sediment load mutations exhibit a spatial lag-transfer pattern. Sediment loads of the three stations show extremely significant decreasing trends across all hydrological periods, and their mutation times display a spatial lag-transfer characteristic: Shadaoguan Station occurs earliest (1969), followed by Xinjiangkou Station (2000), and Zhicheng Station latest (2001), with the largest sediment reduction detected at Shadaoguan Station. A critical turning point took place after 2003. Diversion ratios decrease systematically across periods under comparable inflow conditions, with the most remarkable decline observed in the eastern branch. The positive correlation between sediment-diversion ratio and main-stem sediment load disappears, and the sediment-diversion ratio rises instead despite the reduction of main-stem sediment load. Correspondingly, the sediment transport mechanism shifts from a supply-limited pattern controlled by upstream sediment supply to a capacity-limited pattern governed by flow sediment-carrying capacity. Under annual maximum flood-peak conditions, the diversion capacity of the western branch is enhanced, which is associated with scour at the inlet reach and the development of the left branch of Guanzhou Bar. This study reveals the differentiated response mechanisms of flow-sediment diversion in distributary channels under anthropogenic disturbances, and provides scientific references for river-lake regulation and flood control of the Jingjiang-Dongting Lake system.关键词：Jingjiang River;Songzikou Outlet;flow and sediment diversion;trend test;change-point detection8|0|0更新时间：2026-09-07
- 摘要：Under the backdrop of climate change, extreme rainfall events such as typhoon-induced rainstorms frequently occur in tropical monsoon climate zones, where flood processes exhibit characteristics like strong suddenness, high peaks, and rapid evolution, imposing higher demands on the accuracy of inflow flood forecasting. This study selects the Gozhen River Basin within the tropical monsoon climate zone as the research object, using 42 flood events from 2010 to 2025 as model samples. Two forecasting schemes are constructed: a semi-distributed General Unit Hydrograph (GUH) model centered on generalized unit hydrograph routing, and a Xin'an River model (XAJ) based on the saturation-excess runoff generation mechanism. Inflow flood processes are simulated, incorporating the Muskingum method to account for channel routing effects of upstream reservoir outflows, followed by model performance evaluation. Results show both models effectively simulate flood processes, with average Nash-Sutcliffe coefficients exceeding 0.70 during the validation period. The XAJ model demonstrates superior performance in flood volume fitting. The findings provide references for inflow flood forecasting and reservoir flood control scheduling under typhoon-rainstorm conditions.关键词：reservoir inflow flood forecasting;tropical monsoon climate region;General Unit Hydrograph;Xinanjiang model;Muskingum method;Gezhen Reservoir13|0|0更新时间：2026-09-07
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