Journal of Hydrology最新文献_第9页

A heterogeneous weighting strategy for leveraging Cross-Basin data enhances the Usability of deep learning hydrological models 利用跨流域数据的异构加权策略增强了深度学习水文模型的可用性

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-04-01 Epub Date: 2026-02-05 DOI: 10.1016/j.jhydrol.2026.135097

Sunghyun Yoon , Dongkyun Kim , Kuk-Hyun Ahn

Numerous deep learning (DL) models have been introduced to enhance the reliability of hydrological predictions. Recent studies demonstrate that leveraging large training datasets can substantially improve generalization performance by providing greater opportunities to capture fundamental processes. However, indiscriminate use of all available data in training regional DL models may compromise performance at the local scale. This study addresses this challenge by developing an enhanced network model that leverages cross-basin data. We introduce a novel heterogeneous weighting strategy designed to optimize DL model training for individual basins. The strategy quantifies the influence of one basin’s gradient on the loss of a target basin, thereby offering insights into inter-basin relationships. The strategy was evaluated using 531 basins from the CAMELS-US dataset and an additional 1,147 basins from the CAMELS-DE dataset as reference data. Results reveal that regional pooling models frequently suffer from local performance degradation, whereas the proposed heterogeneous weighting method delivers improved predictions. Compared with the conventional homogeneous weighting strategy, which treats all basins equally, our approach achieved a 0.028 increase in median Kling–Gupta Efficiency, a difference that is statistically significant at the 99.9% confidence level. Importantly, results modeling over 1,678 basins across two countries show that carefully designed training strategies provide greater gains than simply adding additional input variables to the network. Overall, our findings highlight the necessity of frameworks that explicitly account for basin-specific characteristics. By doing so, the proposed strategy advances predictive capabilities at both local and global scales.

许多深度学习（DL）模型已经被引入来提高水文预测的可靠性。最近的研究表明，利用大型训练数据集可以通过提供更多的机会来捕获基本过程，从而大大提高泛化性能。然而，在训练区域深度学习模型时，不加选择地使用所有可用的数据可能会损害局部尺度上的性能。本研究通过开发利用跨盆地数据的增强型网络模型来解决这一挑战。我们引入了一种新的非均匀加权策略，旨在优化单个盆地的深度学习模型训练。该策略量化了一个盆地的坡度对目标盆地损失的影响，从而提供了对盆地间关系的见解。采用CAMELS-US数据集中的531个盆地和CAMELS-DE数据集中的1147个盆地作为参考数据，对该策略进行了评估。结果表明，区域池化模型经常受到局部性能下降的影响，而提出的异构加权方法提供了改进的预测。与同等对待所有盆地的传统均匀加权策略相比，我们的方法使克林-古普塔效率中位数提高了0.028，这一差异在99.9%的置信水平下具有统计学意义。重要的是，对两个国家的1,678个流域进行建模的结果表明，精心设计的训练策略比简单地向网络中添加额外的输入变量提供了更大的收益。总的来说，我们的发现强调了明确解释盆地特定特征的框架的必要性。通过这样做，拟议的战略提高了地方和全球范围的预测能力。

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引用次数: 0

Improving water quality prediction through landscape patterns in Euclidean distance-based drainage areas for plain river networks 利用基于欧氏距离的平原河网流域景观格局改进水质预测

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-04-01 Epub Date: 2026-02-05 DOI: 10.1016/j.jhydrol.2026.135089

Xiaoyu Jiang , Dongli She , Xuan Huang , Yongchun Pan , Yongqiu Xia

Complex geographical characteristics and human interventions in plain river networks lead to ambiguous flow directions and watershed boundaries. This hydrological complexity complicates the accurate assessment of landscape-water quality relationships, particularly in determining the appropriate spatial scale for analysis. Traditional spatial delineation methods have significant limitations in these regions: riparian buffers are not feasible due to the unavailability of flow data, while circular buffers ignore hydrological connectivity. Grounded in the principle that surface pollutants follow least-resistance paths approximated by the Euclidean distance to the nearest water body in flat landscapes, this study developed a Euclidean distance-based drainage area delineation method that assigns each land grid cell to its nearest river segment. This approach successfully delineates contributing areas without the need for unreliable flow-direction data, while inherently considering hydrological connectivity. Applied to the Taihu Basin, water quality data from 86 monitoring stations were classified into three zones (high-quality, moderate, degraded) using Self-Organizing Map. Water quality prediction through landscape pattern analysis was performed using four machine learning algorithms (LightGBM, Random Forest, XGBoost, and Support Vector Machine), in which Euclidean distance–based methods outperformed traditional circular buffer approaches, with LightGBM achieving 0.72 prediction accuracy. SHAP analysis revealed spatially differentiated driver patterns: “landscape-human dominant” in high-quality areas (explaining 80.3% of model variance), “landscape-meteorological dominant” in transition zones (81.9%), and “landscape-human dominant enhancement” in degraded regions (93.8%). Critical ecological thresholds were identified: water body patch size (LPI_40 > 4.9%), urban aggregation (PLADJ_20 < 78.0%), and population density (>8.6 persons/ha). This approach enhances water quality prediction through landscape patterns and identifies critical ecological thresholds, enabling targeted management strategies for aquatic ecosystem protection in plain river networks.

平原河网复杂的地理特征和人为干预导致水流方向和流域边界模糊。这种水文复杂性使景观-水质关系的准确评估变得复杂，特别是在确定适当的空间尺度进行分析时。传统的空间划分方法在这些区域具有明显的局限性：由于无法获得流量数据，河岸缓冲区不可行，而圆形缓冲区忽略了水文连通性。基于地表污染物遵循平坦景观中欧几里得距离到最近水体近似的最小阻力路径的原则，本研究开发了一种基于欧几里得距离的流域划分方法，该方法将每个陆地网格单元分配给其最近的河段。这种方法在不需要不可靠的流向数据的情况下成功地描绘了贡献区域，同时固有地考虑了水文连通性。应用自组织图将太湖流域86个监测站的水质数据划分为优质、中等、退化3个区。采用四种机器学习算法（LightGBM、Random Forest、XGBoost和Support Vector machine）进行景观格局分析的水质预测，其中基于欧里得距离的方法优于传统的圆形缓冲方法，LightGBM的预测精度达到0.72。SHAP分析揭示了空间差异驱动模式：高质量地区为“景观-人文优势”（解释80.3%的模型方差），过渡带为“景观-气象优势”（解释81.9%），退化地区为“景观-人文优势增强”（解释93.8%）。关键生态阈值分别为水体斑块大小（LPI_40 > 4.9%）、城市聚集度（PLADJ_20 < 78.0%）和人口密度（>；8.6人/ha）。该方法通过景观格局加强了水质预测，并确定了关键的生态阈值，为平原河网的水生生态系统保护提供了有针对性的管理策略。

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引用次数: 0

A distributed monitoring method for soil water content based on actively heated optical frequency domain reflectometry and physics-informed neural networks 基于主动加热光频域反射和物理信息神经网络的土壤含水量分布式监测方法

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-04-01 Epub Date: 2026-02-05 DOI: 10.1016/j.jhydrol.2026.135099

Lin Cheng , Wenqi Gao , Dongyan Jia , Junrui Chai , Hao Peng , Xiao Han

In situ monitoring of soil water content using actively heated optical frequency domain reflectometry offers high spatiotemporal resolution. However, the accurate retrieval of soil water content from complex thermal response data remains challenging. Conventional theoretical models, limited by idealized physical assumptions, often fail to characterize heat transfer in multiphase porous media. Similarly, purely data-driven inversion methods often lack physical consistency and robustness. To address these issues, this study developed a physics-informed neural network framework validated through laboratory experiments using sand columns. The framework integrates macroscopic physical principles from asymptotic heat transfer analysis into a correlation-based loss function to mitigate measurement noise sensitivity. Specifically, the model incorporates a fixed thermal contact resistance parameter to decouple sand intrinsic properties from interface effects. This enables a submodel to determine the thermal conductivity–water content (

λ - w

) relationship without prior calibration. Experimental validation shows that the model achieves high accuracy for soil water content (R² = 0.92, MAE = 0.0125 cm³·cm⁻³) and improved robustness compared to benchmark models. The identified

λ - w

function was validated against independent, ground-truth measurements of the sand’s thermal conductivity, confirming it captures the correct physical trend. This work provides a reliable approach for the distributed retrieval of water content in coarse-grained media with high physical consistency and interpretability.

利用主动加热的光学频域反射法原位监测土壤含水量提供了高时空分辨率。然而，从复杂的热响应数据中准确提取土壤含水量仍然具有挑战性。传统的理论模型受理想物理假设的限制，往往不能表征多相多孔介质中的传热。同样，纯数据驱动的反演方法往往缺乏物理一致性和鲁棒性。为了解决这些问题，本研究开发了一个物理信息神经网络框架，并通过砂柱的实验室实验进行了验证。该框架将宏观物理原理从渐近传热分析集成到基于相关的损失函数中，以减轻测量噪声的敏感性。具体来说，该模型包含了一个固定的热接触电阻参数，以将砂的内在特性与界面效应分离开来。这使得子模型无需事先校准即可确定热导率-含水量（λ-w）关系。实验验证表明，与基准模型相比，该模型具有较高的土壤含水量准确度（R2 = 0.92, MAE = 0.0125 cm3·cm⁻3），鲁棒性得到了提高。所识别的λ-w函数通过独立的、真实的砂土导热系数测量结果进行了验证，确认其捕获了正确的物理趋势。这项工作为粗粒度介质中含水量的分布式检索提供了一种可靠的方法，具有高物理一致性和可解释性。

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引用次数: 0

Understanding the limits of two lumped hydrological models through divergences between daily and sub-daily projections 通过日预估和次日预估之间的差异了解两种集总水文模型的局限性

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-04-01 Epub Date: 2026-02-02 DOI: 10.1016/j.jhydrol.2026.135081

Virginie Destuynder, Siavash Pouryousefi Markhali, Annie Poulin

The present paper identifies four common reasons for divergences between projections of flow metrics simulated by lumped conceptual hydrological models at a daily time step, and a daily-averaged 3-h time step. These reasons are based on five case studies simulated by two hydrological models in North America in the context of climate change, and are related to: the filtering of subdaily discrete and cyclic processes; the time-step dependent solving of nonlinear equations involving logical conditions; the time-step dependent solving of nonlinear sequential equations; and the timing of the flood events within the daily window. The time-step dependent simulation of the corresponding internal processes requires compensatory mechanisms in other processes in order to achieve an equivalent performance of the hydrological models in calibration at both time steps. These compensatory mechanisms can be identified in the parameter sets of the models and often imply a time-step dependent seasonality of the internal variables. Divergences between hydrological projections at daily and subdaily time steps reveal significant future shifts in the hydrological regime or in the runoff-generating processes, outside the domain of validity of the calibrated parameter sets. Time-step dependent projections underscore the need for detailed insight before using projections for engineering applications. Furthermore, the comparison of hydrological simulations from different time steps is a valuable approach to assess the domain of validity of calibrated parameter sets, and to understand the behavior of conceptual hydrological models under non-stationary climate conditions.

本文确定了由集总概念水文模型在每日时间步长和每日平均3小时时间步长模拟的流量度量预测之间存在分歧的四个常见原因。这些原因是基于气候变化背景下北美两个水文模型模拟的五个案例研究得出的，它们与以下因素有关：亚日离散和循环过程的过滤；涉及逻辑条件的非线性方程的随时间步长的求解非线性序列方程的随时间步长解以及每天发生洪水的时间。相应内部过程的时间步长依赖模拟需要其他过程中的补偿机制，以便在两个时间步长的校准中实现水文模型的等效性能。这些补偿机制可以在模型的参数集中识别出来，通常意味着内部变量的时间步相关季节性。日和次日时间步的水文预测之间的差异揭示了在校准参数集的有效性范围之外，水文状态或径流产生过程的重大未来变化。依赖于时间步长的投影强调了在工程应用中使用投影之前需要详细的洞察力。此外，比较不同时间步长的水文模拟是评估校准参数集的有效性域和了解非平稳气候条件下概念水文模型行为的有价值的方法。

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引用次数: 0

Extreme precipitation in eastern China: a centennial-scale analysis across multiple river basins based on return period thresholds 中国东部极端降水：基于回归期阈值的多流域百年尺度分析

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-04-01 Epub Date: 2026-02-02 DOI: 10.1016/j.jhydrol.2026.135058

Yichen Yang , Hanwei Yang , Yue Ma , Yong Zhao , Yuanbiao She , Chikun Luo

Under global warming, China has experienced frequent extreme precipitation events, with growing spatial disparities among various river basins. Traditional methods for identifying extreme precipitation based on percentile thresholds (PTs; e.g., R95p or R99p) have proven insufficient sensitivity in responding to changes in precipitation extremes. This study introduces “return period thresholds” (RPT) from hydrology for extreme precipitation definition and verifies their superior sensitivity via comparative analysis in six major eastern Chinese river basins. Defining heavy and extreme heavy precipitation by 0.5a and 2a return periods, respectively, RPT better reflects actual changes in extreme precipitation than PT. For instance, an upward trend in extreme precipitation has been observed throughout the Songhua River Basin, the central and southern Huai River Basin and the middle reaches of the Yangtze River Basin, consistent with the meridional tripolar pattern (8–10a interdecadal oscillations) of the first mode of Empirical Orthogonal Function (EOF). The second mode (EOF2) displays a southward-shifted dipole structure with dominant 5–6a cycles, historically correlated with flood occurrences. Projections to 2100 indicate an upward trajectory in thresholds across all basins, with two salient characteristics: larger absolute increases in southern compared to northern basins, and faster growth rates for extreme heavy precipitation relative to heavy precipitation. These findings underscore the need for enhanced monitoring of both the frequency and intensity of extremes, particularly within southern basins.

在全球变暖背景下，中国极端降水事件频发，且各流域间的空间差异越来越大。基于百分位阈值（PTs，如R95p或R99p）识别极端降水的传统方法已被证明在响应极端降水变化方面灵敏度不足。本研究从水文学中引入“回归期阈值”（RPT）来定义极端降水，并通过对中国东部6个主要流域的对比分析，验证了其优越的敏感性。RPT分别以0.5a和2a回期来定义强降水和极端强降水，比PT更能反映极端降水的实际变化。例如，整个松花江流域、淮河中南部和长江中游地区的极端降水都呈上升趋势；与经验正交函数（EOF）第一模态的经向三极型（8-10a年代际振荡）一致。第二模态（EOF2）表现为南移偶极子结构，以5-6a旋回为主，历史上与洪水发生相关。到2100年的预估表明，所有盆地的阈值都呈上升趋势，并具有两个显著特征：南部盆地的绝对增幅大于北部盆地，极端强降水的增长率相对于强降水更快。这些发现强调需要加强对极端事件频率和强度的监测，特别是在南部盆地内。

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引用次数: 0

Parameterization of complex geological models with PCA‑guided adversarial diffusion for ensemble data assimilation 复杂地质模型的参数化与PCA引导的对抗扩散集成数据同化

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-04-01 Epub Date: 2026-01-27 DOI: 10.1016/j.jhydrol.2026.135044

Wenhao Fu , Yuntian Chen , Zhongzheng Wang , Qiang Zheng , Dongxiao Zhang

Data assimilation of complex geological models in subsurface flow (e.g., channelized models) remains a challenging task due to the non-Gaussian characteristics and high dimensionality of model parameters. Effective parameterization is essential to preserving plausible geological features while reducing model complexity, which in turn improves the efficiency and accuracy of ensemble-based data assimilation workflow. Advances in generative modeling, particularly diffusion models, hold great promise for capturing complex geological structures, but their iterative sampling procedures are prohibitively time-consuming for data assimilation. To address this limitation, we propose principal component analysis-guided adversarial diffusion (PCA-GAD), a single-step diffusion-based generative framework that combines principal component analysis (PCA) with adversarial training. PCA-GAD first constructs a full-grid facies approximation from PCA coefficients to capture dominant channel geometries, and then refines this coarse seed in a single inference pass with a diffusion network trained using an adversarial loss. By leveraging PCA guidance, PCA-GAD preserves key geological structures while substantially reducing parameter dimensionality. Furthermore, in contrast to multi-step diffusion sampling, the adversarial accelerated framework requires only a single inference pass, dramatically shortening sampling time and thereby enabling much faster ensemble-based data assimilation. We demonstrate the effectiveness of the proposed method on two representative cases: a binary facies model and a bimodal permeability model. Compared with conventional parameterization techniques, PCA-GAD better reproduces complex geological structures and achieves more accurate data assimilation. This approach provides a reliable and effective way for uncertainty quantification in subsurface flow modeling under complex geological conditions.

由于模型参数的非高斯特性和高维性，复杂地下流地质模型（如河道化模型）的数据同化仍然是一项具有挑战性的任务。有效的参数化对于在保留合理地质特征的同时降低模型复杂性至关重要，从而提高基于集成的数据同化工作的效率和准确性。生成模型的进步，特别是扩散模型，对捕获复杂的地质结构有着巨大的希望，但它们的迭代采样过程对于数据同化来说过于耗时。为了解决这一限制，我们提出了主成分分析引导的对抗扩散（PCA- gad），这是一种基于扩散的单步生成框架，将主成分分析（PCA）与对抗训练相结合。PCA- gad首先从PCA系数构建一个全网格相近似，以捕获主要通道几何形状，然后使用使用对抗损失训练的扩散网络在单个推理通道中细化该粗种子。通过利用PCA指导，PCA- gad保留了关键的地质结构，同时大大降低了参数维数。此外，与多步扩散采样相比，对抗性加速框架只需要一次推理通过，大大缩短了采样时间，从而实现了更快的基于集成的数据同化。在二元相模型和双峰渗透率模型这两个典型案例中，我们证明了该方法的有效性。与传统的参数化技术相比，PCA-GAD能更好地再现复杂的地质构造，实现更精确的数据同化。该方法为复杂地质条件下地下流模型的不确定性量化提供了可靠有效的方法。

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引用次数: 0

Seasonal shifts in phytoplankton community assembly mediated by hydrological connectivity in a sluice-regulated distributary estuarine network—as a case in the Pearl River Estuary 水闸调节分流河口网络中水文连通性介导的浮游植物群落群落的季节变化——以珠江口为例

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-04-01 Epub Date: 2026-01-27 DOI: 10.1016/j.jhydrol.2026.135038

Yanzi Cai , Xia Li , Guogui Chen , Yiliang Xie , Yujia Zhai , Tian Xie , Mingyu Wang , Fang Gao , Meili Feng , Ying Man , Baoshan Cui

Estuarine deltas are global biodiversity hotspots where hydrological connectivity plays a pivotal role in shaping ecosystem structure and functioning. However, hydrological connectivity in many estuaries has been profoundly altered by freshwater regulation and tidal intrusion, and its effects on community assembly remain poorly understood. Using the Pearl River Estuary as a model system, this study integrates flow-based functional and path-based structural connectivity to investigate how hydrological connectivity mediates phytoplankton community assembly under contrasting hydrographic regimes. Hydrological connectivity exhibited pronounced spatiotemporal heterogeneity, with functional connectivity showing clear spatial clustering during the wet season but declining sharply and becoming fragmented in the dry season. Structural connectivity presented a distinct spatial pattern, concentrated in the central and eastern delta. Phytoplankton communities exhibited marked seasonal contrasts: during the wet season, river-dominated conditions enhanced environmental filtering and species turnover, resulting in high spatial heterogeneity (βsor: 0.65 ± 0.11), while in the dry season, tidal intrusion and reduced freshwater inflow intensified environmental constraints and dispersal limitation, leading to stress-tolerant assemblages and elevated total beta diversity (βsor: 0.70 ± 0.15). Hydrological connectivity significantly modulated the balance between environmental and spatial processes, displaying season-dependent, nonlinear threshold-like responses along connectivity gradients. By explicitly distinguishing functional and structural connectivity, these findings advance a process-based framework for interpreting hydrological–ecological interactions in sluice-regulated distributary estuarine networks, with broader relevance for understanding biodiversity dynamics under anthropogenic pressures.

河口三角洲是全球生物多样性热点地区，其水文连通性对生态系统结构和功能的形成起着关键作用。然而，许多河口的水文连通性已经被淡水调节和潮汐入侵深刻地改变，其对群落聚集的影响仍然知之甚少。本研究以珠江口为模型系统，结合基于流量的功能连通性和基于路径的结构连通性，探讨不同水文条件下水文连通性对浮游植物群落聚集的影响。水文连通性表现出明显的时空异质性，功能连通性在雨季表现出明显的空间聚集性，而在旱季则急剧下降并趋于碎片化。构造连通性呈现出明显的空间格局，主要集中在中东部三角洲。浮游植物群落表现出明显的季节差异：在雨季，河流主导的条件增强了环境过滤和物种转换，导致了较高的空间异质性（β值：0.65±0.11）；而在旱季，潮汐入侵和淡水流入减少加剧了环境约束和扩散限制，导致了耐应力组合和总β值的增加（β值：0.70±0.15）。水文连通性显著调节了环境和空间过程之间的平衡，在连通性梯度上表现出季节依赖的非线性阈值响应。通过明确区分功能和结构上的连通性，这些发现提出了一个基于过程的框架，用于解释水闸调节的分流河口网络中的水文-生态相互作用，这对理解人为压力下的生物多样性动态具有更广泛的意义。

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引用次数: 0

Dynamic resilience quantification of urban drainage networks 城市排水网络动态弹性量化研究

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-04-01 Epub Date: 2026-01-20 DOI: 10.1016/j.jhydrol.2026.134995

Ahmed Abdelaal , Sonia Hassini

The resilience of urban drainage systems (UDS) is increasingly recognized as a critical component in sustainable urban infrastructure, particularly under accelerating urbanization and climate change. Existing resilience assessment methods often rely on static, system-wide overflow metrics, overlooking spatiotemporal variability and component-level performance. This can mask critical vulnerabilities such as pipe surcharging, a major contributor to basement flooding. This study introduces a novel Bottom-Up System Resilience Assessment (BUSRA) framework that quantifies dynamic resilience at the pipe level using hydraulic performance metrics, rather than system overflow alone. BUSRA computes time-resolved cumulative resilience trajectories for each pipe and aggregates them into minimum, final, and system-level indicators, linking local behaviour to network-scale performance. BUSRA is applicable to single-event and continuous rainfall scenarios and to separate or combined sewer systems. The framework was applied to an urban drainage system in Kitchener, Ontario, Canada. Its system-level indicators were benchmarked against the Global Resilience Analysis method for consistency, while BUSRA provides additional component-level and temporal insights beyond what global metrics capture. Key findings include: (1) resilience should be treated as dynamic, as temporally aggregated metrics overestimate system performance; (2) spatial aggregation obscures localized vulnerabilities, whereas BUSRA reveals component-specific weaknesses; and (3) absence of surface flooding does not guarantee resilience, as internal surcharging can still cause significant damage. By delivering time-series and map-based diagnostics at multiple scales, BUSRA enables targeted practical interventions, such as pipe upsizing, storage, and Low Impact Development deployment, and supports adaptive, risk-informed infrastructure planning.

城市排水系统（UDS）的复原力日益被认为是可持续城市基础设施的关键组成部分，特别是在城市化和气候变化加速的情况下。现有的弹性评估方法通常依赖于静态的、系统范围的溢出度量，忽略了时空变异性和组件级性能。这可以掩盖关键的漏洞，如管道附加费，这是地下室淹水的主要原因。本研究引入了一种新颖的自下而上系统弹性评估（BUSRA）框架，该框架使用水力性能指标来量化管道层面的动态弹性，而不仅仅是系统溢流。BUSRA计算每个管道的时间分辨累积弹性轨迹，并将其汇总为最小、最终和系统级指标，将本地行为与网络规模性能联系起来。BUSRA适用于单一事件和连续降雨情况，以及分开或合并的下水道系统。该框架应用于加拿大安大略省基奇纳市的一个城市排水系统。它的系统级指标是根据全球弹性分析方法的一致性进行基准测试的，而BUSRA提供了超出全球指标捕获的额外的组件级和时间洞察力。主要发现包括：(1)弹性应被视为动态的，因为时间聚合指标高估了系统性能；(2)空间聚集掩盖了局部脆弱性，而BUSRA揭示了组件特定的脆弱性；(3)没有地表洪水并不能保证弹性，因为内部溢出仍然会造成重大损害。通过在多个尺度上提供时间序列和基于地图的诊断，BUSRA可以实现有针对性的实际干预，例如管道扩建、存储和低影响开发部署，并支持适应性、风险知情的基础设施规划。

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引用次数: 0

Microbial N2O production and the functional communities regulated by groundwater flow regime 地下水流动对微生物N2O产量及功能群落的调节

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-04-01 Epub Date: 2026-02-10 DOI: 10.1016/j.jhydrol.2026.135116

Yaqi Wang , Helin Wang , Lin Zhang , Yu Han , Ping Li , Yanxin Wang

Groundwater represents a potentially important source of nitrous oxide (N₂O) emissions due to its active nitrogen processes along with groundwater extraction and natural discharge. However, the mechanism regulating N₂O production in response to groundwater flow regimes remains poorly understood. This study integrated ¹⁵N isotope tracing, metagenomic analysis, quantitative polymerase chain reaction (qPCR), and microbial cultivation to investigate the pathways of N₂O production and the functional microbial communities in the groundwater system of the Jianghan Plain, China. In the recharge zone, ammonia oxidation, predominantly mediated by ammonia-oxidizing archaea (AOA), was the dominant source of N₂O, with ammonium nitrogen (NH₄⁺-N) contributing 66% of the total. In the discharge zone, heterotrophic denitrification was the dominant pathway, and nitrate nitrogen (NO₃⁻-N) accounted for 92% of N₂O production. Metagenomic analysis confirmed the differential distribution of key functional genes (amoA, nir, norB) and revealed a general lack of norB genes among nitrifying microorganisms, indicating their limited capacity to produce N₂O via nitrifier denitrification. The high N₂O concentrations and production rates observed in the discharge zone further identified the area as a hotspot for N₂O production, with high potential for subsequent emission. This study demonstrated that the microbial N₂O production and related functional communities were regulated by biogeochemical gradients driven by groundwater flow. These findings improve our understanding of subsurface N₂O production, and highlight the importance of groundwater system in regional and global N₂O budgets.

地下水是一氧化二氮（N2O）排放的潜在重要来源，因为它的活性氮过程以及地下水的开采和自然排放。然而，地下水流动机制对N2O生成的调节机制仍知之甚少。采用15N同位素示踪、宏基因组分析、定量聚合酶链反应（qPCR）和微生物培养等方法，对江汉平原地下水系统N2O生成途径和功能微生物群落进行了研究。在补给区，氨氧化主要由氨氧化古菌（AOA）介导，其中铵态氮（NH4+-N）贡献66%。在排放区，异养反硝化是主要途径，硝态氮（NO3−-N）占N2O产量的92%。宏基因组分析证实了关键功能基因（amoA, nir, norB）的差异分布，并揭示了硝化微生物中普遍缺乏norB基因，表明它们通过硝化器反硝化产生N2O的能力有限。排放区内观察到的高N2O浓度和产率进一步确定了该地区是N2O生产的热点地区，具有较高的后续排放潜力。研究表明，地下水驱动的生物地球化学梯度对微生物N2O产量及相关功能群落具有调控作用。这些发现提高了我们对地下N2O生成的认识，并强调了地下水系统在区域和全球N2O预算中的重要性。

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引用次数: 0

Cross-sectional average velocity predictions for double-layered vegetated open channels incorporating vegetation sheltering and blockage effects 考虑植被遮挡和阻塞效应的双层植被明渠的横断面平均流速预测

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-04-01 Epub Date: 2026-02-03 DOI: 10.1016/j.jhydrol.2026.135076

Yecong Liu, Mengyang Liu, Wenxin Huai, Yidan Ai, Liu Yang, Zhonghua Yang

Investigating the cross-sectional average velocity in open channel flows with double-layered vegetation is pivotal for evaluating flood discharge capacity in real river engineering. Utilizing genetic programming (GP), a machine learning technique, and building upon the Chezy formula structure, this study innovatively incorporates parameters characterizing vegetation sheltering and blockage effects to develop a cross-sectional average velocity predictive model balancing accuracy and computational efficiency. Analysis of the influence of vegetation-related model parameters on the Chezy coefficient C confirmed the model’s physical soundness. Comparative assessment against existing analytical velocity distribution models demonstrated the superior performance of the proposed GP model across multiple evaluation metrics. Furthermore, the study explores potential limitations in traditional velocity distribution models, highlighting the advantages of the GP approach. Specifically, the GP model establishes a robust mapping between hydraulic and geometric parameters to cross-sectional average velocity without relying on empirical vegetation drag coefficients, while effectively capturing vegetation-induced sheltering and blockage effects. In conclusion, this research provides an effective tool for predicting average velocity in rivers with complex vegetation, offering practical guidance for assessing flood discharge capacity in ecological river engineering.

在实际河流工程中，研究具有双层植被的明渠水流的断面平均流速是评价河道泄洪能力的关键。本研究利用遗传规划（GP）这一机器学习技术，在Chezy公式结构的基础上，创新性地引入表征植被遮挡和阻塞效应的参数，建立了平衡精度和计算效率的横截面平均速度预测模型。分析了植被相关模型参数对Chezy系数C的影响，证实了模型的物理合理性。与现有的分析速度分布模型进行了对比评估，证明了所提出的GP模型在多个评价指标上的优越性能。此外，该研究还探讨了传统速度分布模型的潜在局限性，突出了GP方法的优势。具体而言，GP模型在不依赖于经验植被阻力系数的情况下，建立了水力和几何参数与横截面平均流速之间的鲁棒映射，同时有效地捕获了植被引起的遮挡和阻塞效应。总之，本研究为复杂植被河流平均流速预测提供了有效工具，为生态河流工程的泄洪能力评价提供了实践指导。

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引用次数: 0