Journal of Hydrology-Regional Studies最新文献_第4页

Soil erosion and nitrogen loss characteristics of gravel-containing sloping farmland in the three gorges reservoir area 三峡库区含砾坡耕地土壤侵蚀与氮素流失特征

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-01-21 DOI: 10.1016/j.ejrh.2026.103165

Ruzhang Gao , Bingqin Zhao , Jiwei Wang , Xingfeng Zhang , Hai Xiao , Lun Zhang , Daxiang Liu , Dong Xia , Zhenyao Xia , Wennian Xu

Study region

Three Gorges Reservoir (TGR) area, China.

Study focus

Soil erosion and associated nitrogen loss from sloping farmland present a critical global environmental challenge. However, research on how hydrodynamic characteristics influence soil erosion and nitrogen loss in gravel-containing sloping farmland remains limited. To address this knowledge gap, we conducted simulated rainfall experiments on gravel-containing sloping farmlands to further clarify the regulatory roles and impact pathways of hydrodynamic parameters in processes.

New hydrological insights

Gravel content significantly reduced infiltration rate and increased runoff rate. Sediment yield peaked at 20 % gravel content. Nitrate nitrogen (NO₃^-) loss in runoff consistently exceeded ammonium nitrogen (NH₄⁺) loss. Gravel notably increased Stream power (ω), and the enhanced runoff further intensified the disparity between NO₃^- and NH₄⁺ loss in runoff. Model fitting showed that gravel amplified the effects of rainfall intensity and slope gradient on soil erosion and nitrogen loss. Partial Least Squares Structural Equation Model (PLS-SEM) revealed that experimental variables influenced nitrogen loss both directly through hydrodynamic parameters (path coefficient = 0.346) and indirectly via erosion characteristics mediated by hydrodynamics (path coefficient = 0.389). These insights underscore that managing the hydrodynamic processes, for instance by increasing Darcy-Weisbach friction factor (f), is key to controlling soil erosion and nitrogen loss in gravel-containing sloping farmland.

研究区域：三峡库区。坡耕地的土壤侵蚀和氮素流失是全球面临的重大环境挑战。然而，水动力特性对含砾坡耕地土壤侵蚀和氮素流失的影响研究仍然有限。为了解决这一知识缺口，我们在含砾石坡耕地上进行了模拟降雨实验，以进一步阐明水动力参数在过程中的调节作用和影响途径。新的水文见解：砾石含量显著降低入渗速率，增加径流速率。产沙量在含砾量为20% %时达到峰值。径流中硝态氮（NO3-）的损失始终大于铵态氮（NH4+）的损失。碎石显著增加了径流功率（ω），径流的增加进一步加剧了径流中NO3-和NH4+损失的差异。模型拟合表明，砾石放大了降雨强度和坡度对土壤侵蚀和氮流失的影响。偏最小二乘结构方程模型（PLS-SEM）表明，实验变量通过水动力参数（路径系数= 0.346）直接影响氮损失，通过水动力介导的侵蚀特性（路径系数= 0.389）间接影响氮损失。这些见解强调，管理水动力过程，例如通过增加达西-韦斯巴赫摩擦系数(f)，是控制含砾石坡耕地土壤侵蚀和氮流失的关键。

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引用次数: 0

Enhancing hydrological simulation and climate change impact assessment for the Poyang Lake Region, China: A novel hybrid SWAT-GCN-BiLSTM framework 基于SWAT-GCN-BiLSTM的鄱阳湖地区水文模拟与气候变化影响评估

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-01-21 DOI: 10.1016/j.ejrh.2026.103145

Xinxin Zheng , Yiwei Luo , Kan Zhang , Zhenying Zeng , Xiaoying Yang , Xiaogang Li

Study Region

This study was conducted in the ecologically critical and climate-sensitive Poyang Lake Region of China.

Study Focus

Global climate change has increased extreme hydrological events worldwide, necessitating advanced hydrological models to manage escalating risks. This study proposed a hybrid model, SWAT-GCN-BiLSTM, integrating the strengths of SWAT (simulating physical hydrological processes), GCN (capturing spatial topological relationships), and BiLSTM (modeling complex temporal dynamics).

New Hydrological Insights

The hybrid SWAT-GCN-BiLSTM model outperformed the standalone SWAT and BiLSTM models, with significantly higher NSE and R² values of around 0.90. The hybrid model particularly excelled in simulating extreme flows, reducing RMSE by over 20 % for extremely high flows (≥ Q10, Q10 represents streamflow magnitude with a 10 % exceedance probability). Based on the ensemble mean of four Global Climate Models, the hybrid model predicted a substantial increase in streamflow during the wet months of April (24.9 %-44.1 %) and May (11.5 %-20.2 %) compared to the baseline. Furthermore, under all considered climate change scenarios, the Q10 of the 7-day flow was projected to increase by 9.5–19.5 %. Conversely, streamflow in the dry months of November and December was projected to decrease by 21.0–34.7 %. This indicates climate change may exacerbate hydrological extremes, necessitating robust adaptive management strategies to address both increased spring flooding risk and heightened drought conditions during late autumn/early winter in the region under a changing climate.

研究区域本研究在中国生态临界和气候敏感的鄱阳湖地区进行。全球气候变化增加了世界范围内的极端水文事件，需要先进的水文模型来管理不断升级的风险。本研究提出了SWAT-GCN-BiLSTM混合模型，将SWAT（模拟物理水文过程）、GCN（捕获空间拓扑关系）和BiLSTM（模拟复杂时间动态）的优势结合起来。混合SWAT- gcn -BiLSTM模型优于单独的SWAT和BiLSTM模型，NSE和R2值明显更高，约为0.90。混合模型在模拟极端流量方面尤其出色，对于极高流量，RMSE降低了20 %以上（≥Q10， Q10代表流量大小，超出概率为10 %）。基于四个全球气候模式的集合平均值，混合模式预测4月（24.9 %-44.1 %）和5月（11.5 %-20.2 %）与基线相比，湿月的流量显著增加。此外，在所有考虑的气候变化情景下，预计7天流量的Q10将增加9.5 - 19.5% %。相反，11月和12月旱季的流量预计减少21.0 - 34.7% %。这表明气候变化可能加剧水文极端事件，需要强有力的适应性管理策略来应对气候变化下该地区春季洪水风险增加和秋末/初冬干旱状况加剧的问题。

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引用次数: 0

Spatio-temporal dynamics and drivers of drying conditions in the HRB: Unraveling the role of precipitation intensity and total precipitation in drying risk 干旱区干旱条件的时空动态和驱动因素：揭示降水强度和总降水量在干旱风险中的作用

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-01-21 DOI: 10.1016/j.ejrh.2026.103126

Yang Cao , Xin Wang , Congsheng Fu , Xianyong Meng , Jiejie Lyu , Jie Zhang , Hui Liu , Huijun Zheng , Hongliang Zhang , Miaomiao Zhang

Study region: The Hangbu River Basin (HRB), located in eastern China, is the largest sub-basin of the Chaohu Lake Basin.

Study focus

In recent decades, the HRB has experienced increased drying and spatial heterogeneity in its hydrological conditions, despite no significant trend in total precipitation in the region. To address this, this study integrates 65 years (1959–2023) of meteorological and hydrological data with the Palmer Drought Severity Index (PDSI), the Soil and Water Assessment Tool (SWAT) hydrological model, and multiple trend analysis methods to reveal the basin’s dry-wet characteristics and the driving mechanisms. The specific objectives are to: (i) analyze the spatio-temporal heterogeneity of precipitation and hydrological components within the basin; and (ii) quantify the spatial differences in dry-wet characteristics and elucidate the underlying precipitation patterns that shape the basin’s hydrological regimes.

New hydrological insights for the region

Our results reveal that (1) The downstream area frequently experiences years with high precipitation intensity (independent variable x) but low total precipitation (dependent variable y) (with a smaller linear slope, k = 36), which exacerbates the drying risk; (2) In contrast, the upstream region experiences years with high precipitation intensity and high total precipitation (with a larger linear slope, k = 189), which reduces the drying risk. Our findings highlight that the linear structure of precipitation is a key determinant of regional wet-dry conditions.

研究区域：汉步河流域位于中国东部，是巢湖流域最大的子流域。近几十年来，尽管该地区总降水量没有明显的变化趋势，但青藏高原的干旱程度和水文条件的空间异质性有所增加。为解决这一问题，本研究将1965年（1959-2023）的气象水文资料与Palmer干旱严重指数（PDSI）、水土评估工具（SWAT）水文模型以及多种趋势分析方法相结合，揭示了流域干湿特征及其驱动机制。具体目标是：(i)分析流域内降水和水文成分的时空异质性；（ii）量化干湿特征的空间差异，阐明形成流域水文制度的潜在降水模式。结果表明：(1)下游地区经常经历降水强度高（自变量x）但总降水量低（因变量y）的年份（线性斜率较小，k = 36），这加剧了干旱风险；(2)上游地区降水强度大、总降水量大（线性斜率较大，k = 189），干旱风险降低。我们的研究结果强调，降水的线性结构是区域干湿条件的关键决定因素。

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引用次数: 0

Improving transparency in karst spring discharge and water quality forecasts using interpretable machine learning models in the Eastern Alps 利用可解释的机器学习模型提高东阿尔卑斯山喀斯特泉流量和水质预测的透明度

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-01-21 DOI: 10.1016/j.ejrh.2026.103147

Anna Pölz , Alfred Paul Blaschke , Katalin Demeter , Günter Blöschl , Margaret E. Stevenson , Helene Bauer , Liping Pang , Andreas H. Farnleitner , Julia Derx

Study region

Karst springs draining the Hochschwab massif, Eastern Alps, Austria.

Study focus

Accurate forecasting of spring discharge and water quality is crucial for sustainable water resource management. Although machine learning (ML) models have shown considerable potential for forecasting hydrological variables, understanding the underlying processes remains limited. This study aimed to improve the transparency of ML models through an attribution analysis, which explores the contribution of local environmental factors to forecasts. Several ML models were deployed to predict spring discharge and water quality, measured by the spectral absorption coefficient at 254 nm (UV254), up to four days in advance at karst springs.

Innovative insights

The Deep SHAP method aided in identifying significant seasonal variations in model attributions, showing the most pronounced changes for snow depth, followed by physicochemical variables such as electrical conductivity and other meteorological variables. The Transformer model exhibited the best overall performance. Model uncertainty, assessed through the Deep Ensemble method, is greater in spring and summer, and both the model errors and uncertainties increase with variability of the target variables. To evaluate model applicability for selective water abstraction, we classified UV254 forecasts based on threshold exceedance, achieving high classification accuracy (>95 % for 1-day and >90 % for 2-day forecasts). Integrating Deep SHAP and Deep Ensemble methods enhanced ML transparency. This combined approach provides insights that can inform drinking water management decisions in karst systems.

研究区域：奥地利东阿尔卑斯山Hochschwab地块的喀斯特泉。研究重点泉水流量和水质的准确预测是水资源可持续管理的关键。尽管机器学习（ML）模型在预测水文变量方面显示出相当大的潜力，但对潜在过程的理解仍然有限。本研究旨在通过归因分析来提高机器学习模型的透明度，该分析探讨了当地环境因素对预测的贡献。利用光谱吸收系数在254 nm （UV254）处测量的几个ML模型，提前4天预测喀斯特泉的流量和水质。Deep SHAP方法有助于识别模式属性中显著的季节变化，显示出雪深最显著的变化，其次是物理化学变量，如电导率和其他气象变量。变形金刚模型表现出最好的综合性能。通过深度集合方法评估的模式不确定性在春季和夏季较大，模式误差和不确定性都随着目标变量的变率而增加。为了评估模型对选择性抽水的适用性，我们基于阈值超越对UV254预测进行了分类，获得了很高的分类精度（1天预测>；95 %，2天预测>；90 %）。集成深度SHAP和深度集成方法增强了机器学习的透明度。这种综合方法可以为喀斯特系统的饮用水管理决策提供信息。

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引用次数: 0

Deciphering the daily spatiotemporal dynamics and mechanisms of floods in the Tarim Basin desert region 解读塔里木盆地荒漠地区洪水日时空动态及机制

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-01-21 DOI: 10.1016/j.ejrh.2026.103158

Arken Tursun , Xianhong Xie , Hossein Azadi , Anwar Eziz , Yibing Wang , Bowen Zhu , Alishir Kurban

Study region

The study focuses on the Tarim River Basin in the hyper arid regions, where flooding events have become increasingly frequent and severe due to climate change. The desert–oasis transition zones are highly vulnerable because of their limited vegetation cover, low soil permeability, and strong hydrological variability, which together complicate flood monitoring and management.

Study focus

Comprehensive flood simulations that simultaneously capture flood extent and streamflow dynamics at high spatiotemporal resolution remain scarce in arid environments. To address this gap, we propose an interpretable deep learning framework for full-process flood modeling. The framework integrates daily 30 m Seamless Data Cube (SDC) remote sensing data with deep learning–based hydrological models. A U-shaped network (UNet) is used to extract daily flood extents, while hybrid and pure deep learning models simulate daily streamflow under data-scarce conditions. The integration of these models enables a consistent representation of flood processes from surface inundation to river discharge.

New hydrological insights for the region

Validation with Landsat imagery confirms that SDC-derived flood maps achieve an average bias below 5 %, while the streamflow simulations produce median Kling–Gupta Efficiency (KGE) and Nash–Sutcliffe Efficiency (NSE) values exceeding 0.8. The proposed framework successfully captures both the spatial and temporal dynamics of floods in arid regions. Furthermore, interpretability analysis reveals that accelerated snowmelt is the dominant driver of recent flood events. This study demonstrates a transferable and data-efficient approach for improving flood modeling and monitoring across arid regions worldwide.

研究区域研究的重点是位于极度干旱区的塔里木河流域，该地区受气候变化的影响，洪水事件日益频繁和严重。荒漠-绿洲过渡带由于植被覆盖有限、土壤渗透性低、水文变异性强，使洪水监测和管理变得十分复杂，因此具有高度脆弱性。研究重点在干旱环境下，能够在高时空分辨率下同时捕获洪水范围和河流动态的综合洪水模拟仍然很缺乏。为了解决这一差距，我们提出了一个可解释的深度学习框架，用于全流程洪水建模。该框架将每日30 m无缝数据立方体（SDC）遥感数据与基于深度学习的水文模型集成在一起。u形网络（UNet）用于提取每日洪水范围，而混合和纯深度学习模型在数据稀缺条件下模拟每日流量。这些模型的整合使得从地表淹没到河流流量的洪水过程得到一致的表示。Landsat图像验证证实，sdc衍生的洪水图的平均偏差低于5 %，而溪流模拟产生的中位数克林-古普塔效率（KGE）和纳什-苏特克利夫效率（NSE）值超过0.8。该框架成功地捕捉了干旱地区洪水的时空动态。此外，可解释性分析表明，加速融雪是最近洪水事件的主要驱动因素。这项研究为改善全球干旱地区的洪水建模和监测提供了一种可转移的、数据高效的方法。

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引用次数: 0

Machine learning-informed vulnerability assessment of coastal erosion: A case study of the Minjiang River Estuary 基于机器学习的海岸侵蚀脆弱性评估——以闽江河口为例

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-01-20 DOI: 10.1016/j.ejrh.2026.103132

Xiaohe Lai , Ningyuan Xu , Jun Jiang , Jianping Jia , Yan Liu , Yan Su , Chuan Lin , Xiudong Xie , Aijun Wang , Feng Cai

Study region

Minjiang River Estuary, China.

Study focus

Reliable assessments of coastal erosion risk are essential for sustainable urban planning. However, existing methods often fail to capture the dynamic, nonlinear interactions of natural and anthropogenic factors, and the "black-box" nature of many machine learning models limits their practical application. Addressing this gap, we developed a dynamic framework to assess long-term coastal erosion vulnerability in the Minjiang River Estuary. Our study integrated multi-temporal data from 16 key erosion-inducing factors over a 30-year period (1990–2020) and employed five machine learning algorithms to enhance both the predictive accuracy and interpretability of the model.

New hydrological insights for the region

New Hydrological Insights for the Region: Results reveal a generally weak erosion trend along the estuary, punctuated by zones of intense local degradation. The Random Forest model achieved the highest accuracy (≥0.92) and AUC (≥0.97), enabling reliable identification of high-risk areas for targeted coastal management interventions, such as shoreline protection and urban planning adjustments. Feature importance analyses indicate watershed-scale land cover and land use (LCLU) dynamics are the dominant drivers of long-term erosion vulnerability, while short-term patterns are shaped by temporally variable factors. These findings highlight the critical value of integrating time-sensitive drivers into coastal risk assessments and underscore the importance of model selection for adaptive urban and environmental management. The proposed approach offers a scalable and transferable methodology for supporting climate-resilient planning in vulnerable coastal cities.

研究区域：中国闽江口。可靠的海岸侵蚀风险评估对可持续城市规划至关重要。然而，现有的方法往往无法捕捉自然和人为因素的动态、非线性相互作用，许多机器学习模型的“黑箱”性质限制了它们的实际应用。为了解决这一问题，我们开发了一个动态框架来评估闽江河口长期海岸侵蚀脆弱性。我们的研究整合了30年间（1990-2020年）16个关键侵蚀诱导因子的多时间数据，并采用了5种机器学习算法来提高模型的预测准确性和可解释性。该地区的新水文见解：研究结果显示，河口沿岸的侵蚀趋势普遍较弱，偶尔会出现局部严重退化的区域。随机森林模型获得了最高的精度（≥0.92）和AUC(≥0.97)，能够可靠地识别高风险区域，以便进行有针对性的海岸管理干预，如海岸线保护和城市规划调整。特征重要性分析表明，流域尺度土地覆盖和土地利用（LCLU）动态是长期侵蚀脆弱性的主要驱动因素，而短期格局则受时间变量的影响。这些发现突出了将时间敏感驱动因素纳入沿海风险评估的关键价值，并强调了模式选择对适应性城市和环境管理的重要性。拟议的方法为支持脆弱沿海城市的气候适应型规划提供了一种可扩展和可转移的方法。

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引用次数: 0

The longitudinal assessment of flood hazard in cities: Unlocking the floodplain record of Houston, TX, USA 城市洪水灾害的纵向评估：解锁美国德克萨斯州休斯顿洪泛区记录

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-01-20 DOI: 10.1016/j.ejrh.2026.103113

Francisco Haces-Garcia , Craig L. Glennie , Hanadi S. Rifai , Vedhus Hoskere

Study Region: Houston, TX, USA

Study Focus: The data-driven quantification of evolving urban flood hazard is challenging. Historical flooding data is readily available from the US National Flood Insurance Program, which has mapped Flood Hazard Areas (FHAs) since the 1970s. However, estimated FHAs are generally not used in modern flood studies due to the lack of georeferencing information. This poses a key impediment for fine-scale floodplain analysis, with critical implications for the study of urban flood change. This research develops a framework to automatically georeference historical Flood Insurance Rate Maps, and extract their floodplain data using photogrammetry, geomatics, and artificial intelligence. The registration framework is systematically validated to ensure the accurate extraction of longitudinal flood data. A median georeferencing residual of 23.1 m was obtained, which was smaller than the validation dataset accuracy. The framework provides an avenue towards the widespread assessment of longitudinal flood hazard, with significant implications for the study of urban flood resilience. Three flood-prone case studies are presented to exemplify the usefulness of the framework; Brays Bayou, Hunting Bayou, and Cypress Creek in Greater Houston.

New hydrological insights for the region: The case studies quantify the change of flood hazard within these watersheds. Floodplain expansion had significant flood resilience consequences. Population exposure was estimated to have risen by up to 635%, with a concurrent increase in the vulnerability of critical infrastructure.

研究重点：城市洪水灾害演变的数据驱动量化是具有挑战性的。历史洪水数据很容易从美国国家洪水保险计划获得，该计划自20世纪70年代以来绘制了洪水危险区（FHAs）地图。然而，由于缺乏地理参考信息，估计的fha通常不用于现代洪水研究。这对精细尺度的洪泛平原分析构成了关键障碍，对城市洪水变化的研究具有重要意义。本研究开发了一个框架来自动地参考历史洪水保险率地图，并利用摄影测量、地理信息和人工智能提取其洪泛平原数据。为保证纵向洪水数据的准确提取，对配准框架进行了系统验证。得到的中位数地理参考残差为23.1 m，小于验证数据集的精度。该框架为纵向洪水灾害的广泛评估提供了一条途径，对城市抗洪能力的研究具有重要意义。提出了三个容易发生洪水的案例研究，以说明该框架的有用性；大休斯顿的Brays Bayou， Hunting Bayou和Cypress Creek。该地区新的水文见解：案例研究量化了这些流域内洪水灾害的变化。洪泛区扩张对洪水恢复能力有显著影响。据估计，人口暴露率上升了635%，同时关键基础设施的脆弱性也在增加。

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引用次数: 0

Impact of deformation response patterns to groundwater level: A post water division project operation in Beijing Plain 变形响应模式对地下水位的影响：北京平原后分水工程运行

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-01-20 DOI: 10.1016/j.ejrh.2026.103154

Xin Li , Lin Zhu , Haigang Wang , Huili Gong , Xiaojuan Li

Study region

The Beijing Plain (BJP), China.

Study focus

With the variable rise of groundwater levels in BJP from 2015 to 2023, deformation patterns and spatio-temporal features have become more complicated. To identify these deformation features and quantify the relationship between deformation and groundwater levels within multiple confined aquifer systems, this study combined K-Shape, BEAST with TFA on basis of the InSAR-derived deformation data. These findings provide scientific support for groundwater management and deformation risk assessment in different deformation pattern regions.

New hydrological insights

Four deformation patterns were identified, including subsidence (Sub), slow subsidence followed by slow rebound (SSFR), slow rebound (SR), and rapid rebound (RR) zones. The Sub zone covered the largest area of 2447 km², while the areas of RR and SSFR were comparable and small with the value of 573 km² and 533 km². In RR zone, uplift was driven by groundwater level recovery with an average increase of 23 m in the first confined aquifer, where the lithology is composed of sand-gravel with low clay content. This type of deposits facilitates elastic rebound with high coherence of 0.63 at 6-month timescale and short lag times of about 16 days. In the Sub zone, subsidence is primarily governed by the second and third confined aquifers. This reflects the legacy of intensified deep-groundwater exploitation and is characterized by high gain and significant lag times of about 104 and 109 days for the second and third confined aquifers, respectively. These phenomena are strongly associated with strata lithology and exploitation history in different deformation pattern zones.

研究区域：中国北京平原。随着2015 - 2023年印度人民党地下水位的变化，其变形模式和时空特征变得更加复杂。为了识别这些变形特征，并量化多个承压含水层系统中变形与地下水位之间的关系，本研究在insar导出的变形数据的基础上，将K-Shape、BEAST和TFA结合起来。研究结果为不同变形模式区域的地下水管理和变形风险评价提供了科学依据。确定了四种变形模式，包括沉降区（Sub）、缓慢沉降后缓慢反弹区（SSFR）、缓慢反弹区（SR）和快速反弹区（RR）。分区面积最大，为2447 km2，而RR和SSFR面积较小，分别为573 km2和533 km2。RR带主要受地下水位恢复驱动，第一承压含水层平均上升23 m，岩性为砂砾质，粘土含量较低。这种类型的沉积物有利于弹性反弹，在6个月的时间尺度上具有0.63的高相干性，滞后时间短，约为16天。在该区，下沉主要受第二和第三承压含水层控制。这反映了深层地下水开采加剧的遗留问题，其特点是高增益，第二和第三承压含水层的滞后时间分别约为104天和109天。这些现象与不同变形模式带的地层岩性和开采历史密切相关。

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引用次数: 0

Synergistic dynamics and driving mechanisms of land cover and evapotranspiration under multi-scenario projections from the Pearl River Basin 珠江流域多情景预测下土地覆盖与蒸散的协同动力学及驱动机制

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-01-20 DOI: 10.1016/j.ejrh.2026.103149

Han Zhong , Benbo Tan , Junyi Zhang , Mingxu Peng , Jianpei Feng , Shichuang Weng , Yujie Zhao , Chuanfu Zang

Study region

The Pearl River Basin (China).

Study focus

This study examined the synergistic dynamics between land cover and evapotranspiration (ET) components, including evaporation (E), transpiration (T), under three scenarios—business as usual (BAU), carbon neutrality target (CNT), and economic development priority (EDP) from 2021 to 2050. Using the Shuttleworth-Wallace-Hu (SWH) model, we simulated E, T and ET at high resolution and quantified the contributions of E and T to ET variations, as well as the interactions among vegetation, climate variables, and topography.

New hydrogeological insights from the region

The study reveals pronounced synergy between land-cover change and ET dynamics across the basin. Under CNT, marked expansion of forest and water bodies coincides with only modest increases in ET, whereas conversion of farmland to urban land in the Greater Bay Area elevates E with negligible change in T. T accounts for a larger share of ET variability than E, indicating T’s central role in regional water–energy coupling. Interaction analysis shows vegetation dominates through synergistic effects with temperature, precipitation and elevation, suggesting a “vegetation–climate–topography” co-regulation mechanism of T. These findings offer empirical evidence and a practical framework for integrated watershed water-resource management and scenario-informed planning.

研究区域：中国珠江流域。本研究考察了2021 - 2050年三种情景下土地覆盖与蒸散（ET）组分(包括蒸发（E)、蒸腾(T)）之间的协同动态关系，即“一切照常”（BAU）、“碳中和目标”（CNT）和“经济发展优先”（EDP）。利用Shuttleworth-Wallace-Hu （SWH）模式模拟了高分辨率的E、T和ET，量化了E和T对ET变化的贡献，以及植被、气候变量和地形之间的相互作用。该研究揭示了整个盆地的土地覆盖变化和ET动态之间明显的协同作用。在CNT下，森林和水体的显著扩张与ET的适度增加相一致，而在大湾区，农田向城市用地的转变使ET升高，而T的变化可以忽略不计，T在ET变率中所占的份额大于E，表明T在区域水能耦合中起着核心作用。交互作用分析表明，植被通过与温度、降水和海拔的协同作用发挥主导作用，表明t存在“植被-气候-地形”协同调节机制。这些发现为流域水资源综合管理和情景规划提供了经验证据和实践框架。

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引用次数: 0

Cascade reservoirs attenuate the seasonality of estuarine tidal duration asymmetry 梯级水库减弱了河口潮时不对称的季节性

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-01-19 DOI: 10.1016/j.ejrh.2026.103153

Jianliang Lin , Haowei Chen , Xianzhao He , Shuai Hu , Linxi Fu , Huayang Cai , Lixia Niu , Xiaohe Zhang , Xiangyuan Li , Ping Zhang , Qingshu Yang

Study region

The Pearl River Estuary in southern China has strong monsoonal wet-dry discharge contrasts and extensive cascade reservoir regulation in its basin.

Study focus

This study quantifies how reservoir-driven discharge re-partitioning affects the seasonality and multi-decadal evolution of tidal duration asymmetry (TDA; the difference between flood and ebb durations). To reveal the linkage between TDA and regulated discharge, we analyze 51 years (1966–2016) of water levels from 21 tide gauges and daily discharge from three upstream stations using a tidal skewness metric, non-stationary harmonic diagnostics, and trend analysis.

New hydrological insights for the region

TDA shows significant seasonality, with higher skewness in the wet season (April–September) and lower skewness in the dry season (October–March), and a basin-wide trough in late June–early July when discharge peaks. The discharge-TDA relation is threshold-dependent: normalized skewness increases with normalized discharge up to ∼0.62 (∼12,550 m³/s) and decreases thereafter. Reservoir regulation reduced wet-season peak flows (−76.3 m³/s/yr) and increased dry-season baseflows (+16.5 m³/s/yr), attenuating seasonal TDA amplitude most strongly in river–tide transition reaches (−0.0036 /yr). Spatial differences indicate phase-controlled TDA seasonality upstream but amplitude-controlled seasonality downstream, consistent with shifts in dominant tidal interactions from K₁–O₁–M₂ to M₂–M₄. The threshold rule links managed discharge to seasonal TDA and supports basin-to-estuary flow management in regulated coastal rivers.

珠江口流域具有强烈的季风干湿对比和广泛的梯级水库调节。本研究量化了水库驱动的流量再分配如何影响潮汐持续时间不对称（TDA）的季节性和多年代际演变。为了揭示TDA与调节流量之间的联系，我们使用潮汐偏度度量、非平稳谐波诊断和趋势分析，分析了51年（1966-2016）21个潮汐计的水位和三个上游站的日流量。数据显示出明显的季节性特征，雨季（4 - 9月）偏度较高，旱季（10 - 3月）偏度较低，6月下旬- 7月初是流域范围内的波谷，此时流量达到峰值。流量- tda关系是阈值相关的：归一化偏度随着归一化流量的增加而增加，最高可达0.62(~ 12,550 m³/s)，此后减小。水库调节降低了湿季峰值流量（−76.3 m³/s/yr），增加了干季基流量（+16.5 m³/s/yr），对季节TDA振幅的衰减在河潮过渡段最为强烈（−0.0036 /yr）。空间差异表明上游TDA季节性受相位控制，下游TDA季节性受幅度控制，与主导潮汐相互作用从K₁-O₁-M₂向M₂-M₄转变相一致。阈值规则将管理排放与季节性TDA联系起来，并支持受管制的沿海河流的流域到河口流量管理。

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引用次数: 0